The genomic rate of molecular adaptation of the human influenza A virus

Eco-evolutionary dynamics of adapting pathogens and host immunity

As pathogens spread in a population of hosts, immunity is built up, and the pool of susceptible individuals are depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host's immune systems adapt, and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high-frequency variants observed for influenza viruses.

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

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.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app .

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis, multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

Defining the balance between optimal immunity and immunopathology in influenza virus infection

Influenza A viruses remain a global threat to human health, with continued pandemic potential. In this Review, we discuss our current understanding of the optimal immune responses that drive recovery from influenza virus infection, highlighting the fine balance between protective immune mechanisms and detrimental immunopathology. We describe the contribution of innate and adaptive immune cells, inflammatory modulators and antibodies to influenza virus-specific immunity, inflammation and immunopathology. We highlight recent human influenza virus challenge studies that advance our understanding of susceptibility to influenza and determinants of symptomatic disease. We also describe studies of influenza virus-specific immunity in high-risk groups following infection and vaccination that inform the design of future vaccines to promote optimal antiviral immunity, particularly in vulnerable populations. Finally, we draw on lessons from the COVID-19 pandemic to refocus our attention to the ever-changing, highly mutable influenza A virus, predicted to cause future global pandemics.

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.

Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2

Public health researchers and practitioners commonly infer phylogenies from viral genome sequences to understand transmission dynamics and identify clusters of genetically-related samples. However, viruses that reassort or recombine violate phylogenetic assumptions and require more sophisticated methods. Even when phylogenies are appropriate, they can be unnecessary or difficult to interpret without specialty knowledge. For example, pairwise distances between sequences can be enough to identify clusters of related samples or assign new samples to existing phylogenetic clusters. In this work, we tested whether dimensionality reduction methods could capture known genetic groups within two human pathogenic viruses that cause substantial human morbidity and mortality and frequently reassort or recombine, respectively: seasonal influenza A/H3N2 and SARS-CoV-2. We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2). For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding. We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages. We found that MDS embeddings accurately represented pairwise genetic distances including the intermediate placement of recombinant SARS-CoV-2 lineages between parental lineages. Clusters from t-SNE embeddings accurately recapitulated known phylogenetic clades, H3N2 reassortment groups, and SARS-CoV-2 recombinant lineages. We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses. Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.

Prospects for a sequence-based taxonomy of influenza A virus subtypes

Hemagglutinin (HA) and neuraminidase (NA) proteins are the primary antigenic targets of influenza A virus (IAV) infections. IAV infections are generally classified into subtypes of HA and NA proteins, e.g. H3N2. Most of the known subtypes were originally defined by a lack of antibody cross-reactivity. However, genetic sequencing has played an increasingly important role in characterizing the evolving diversity of IAV. Novel subtypes have recently been described solely by their genetic sequences, and IAV infections are routinely subtyped by molecular assays, or the comparison of sequences to references. In this study, I carry out a comparative analysis of all available IAV protein sequences in the Genbank database (over 1.1 million, reduced to 272,292 unique sequences prior to phylogenetic reconstruction) to determine whether the serologically defined subtypes can be reproduced with sequence-based criteria. I show that a robust genetic taxonomy of HA and NA subtypes can be obtained using a simple clustering method, namely, by progressively partitioning the phylogeny on its longest internal branches. However, this taxonomy also requires some amendments to the current nomenclature. For example, two IAV isolates from bats previously characterized as a divergent lineage of H9N2 should be separated into their own subtype. With the exception of these small and highly divergent lineages, the phylogenies relating each of the other six genomic segments do not support partitions into major subtypes.

Epistasis mediates the evolution of the receptor binding mode in recent human H3N2 hemagglutinin

The receptor-binding site of influenza A virus hemagglutinin partially overlaps with major antigenic sites and constantly evolves. In this study, we observe that mutations G186D and D190N in the hemagglutinin receptor-binding site have coevolved in two recent human H3N2 clades. X-ray crystallography results show that these mutations coordinately drive the evolution of the hemagglutinin receptor binding mode. Epistasis between G186D and D190N is further demonstrated by glycan binding and thermostability analyses. Immunization and neutralization experiments using mouse and human samples indicate that the evolution of receptor binding mode is accompanied by a change in antigenicity. Besides, combinatorial mutagenesis reveals that G186D and D190N, along with other natural mutations in recent H3N2 strains, alter the compatibility with a common egg-adaptive mutation in seasonal influenza vaccines. Overall, our findings elucidate the role of epistasis in shaping the recent evolution of human H3N2 hemagglutinin and substantiate the high evolvability of its receptor-binding mode.

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.

Prevention of respiratory virus transmission by resident memory CD8+ T cells

An ideal vaccine both attenuates virus growth and disease in infected individuals and reduces the spread of infections in the population, thereby generating herd immunity. Although this strategy has proved successful by generating humoral immunity to measles, yellow fever and polio, many respiratory viruses evolve to evade pre-existing antibodies1. One approach for improving the breadth of antiviral immunity against escape variants is through the generation of memory T cells in the respiratory tract, which are positioned to respond rapidly to respiratory virus infections2-6. However, it is unknown whether memory T cells alone can effectively surveil the respiratory tract to the extent that they eliminate or greatly reduce viral transmission following exposure of an individual to infection. Here we use a mouse model of natural parainfluenza virus transmission to quantify the extent to which memory CD8+ T cells resident in the respiratory tract can provide herd immunity by reducing both the susceptibility of acquiring infection and the extent of transmission, even in the absence of virus-specific antibodies. We demonstrate that protection by resident memory CD8+ T cells requires the antiviral cytokine interferon-γ (IFNγ) and leads to altered transcriptional programming of epithelial cells within the respiratory tract. These results suggest that tissue-resident CD8+ T cells in the respiratory tract can have important roles in protecting the host against viral disease and limiting viral spread throughout the population.

Genomic Analysis of Influenza A and B Viruses Carrying Baloxavir Resistance-Associated Substitutions Serially Passaged in Human Epithelial Cells

Baloxavir marboxil (baloxavir) is an FDA-approved inhibitor of the influenza virus polymerase acidic (PA) protein. Here, we used next-generation sequencing to compare the genomic mutational profiles of IAV H1N1 and H3N2, and IBV wild type (WT) and mutants (MUT) viruses carrying baloxavir resistance-associated substitutions (H1N1-PA I38L, I38T, and E199D; H3N2-PA I38T; and IBV-PA I38T) during passaging in normal human bronchial epithelial (NHBE) cells. We determined the ratio of nonsynonymous to synonymous nucleotide mutations (dN/dS) and identified the location and type of amino acid (AA) substitutions that occurred at a frequency of ≥30%. We observed that IAV H1N1 WT and MUT viruses remained relatively stable during passaging. While the mutational profiles for IAV H1N1 I38L, I38T, and E199D, and IBV I38T MUTs were relatively similar after each passage compared to the respective WTs, the mutational profile of the IAV H3N2 I38T MUT was significantly different for most genes compared to H3N2 WT. Our work provides insight into how baloxavir resistance-associated substitutions may impact influenza virus evolution in natural settings. Further characterization of the potentially adaptive mutations identified in this study is needed.

An atlas of continuous adaptive evolution in endemic human viruses

Through antigenic evolution, viruses such as seasonal influenza evade recognition by neutralizing antibodies. This means that a person with antibodies well tuned to an initial infection will not be protected against the same virus years later and that vaccine-mediated protection will decay. To expand our understanding of which endemic human viruses evolve in this fashion, we assess adaptive evolution across the genome of 28 endemic viruses spanning a wide range of viral families and transmission modes. Surface proteins consistently show the highest rates of adaptation, and ten viruses in this panel are estimated to undergo antigenic evolution to selectively fix mutations that enable the escape of prior immunity. Thus, antibody evasion is not an uncommon evolutionary strategy among human viruses, and monitoring this evolution will inform future vaccine efforts. Additionally, by comparing overall amino acid substitution rates, we show that SARS-CoV-2 is accumulating protein-coding changes at substantially faster rates than endemic viruses.

Phylogenetic identification of influenza virus candidates for seasonal vaccines

The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.

Lack of neutralizing antibodies against influenza A viruses in adults during the 2022/2023 winter season - a serological study using retrospective samples collected in Hong Kong

OBJECTIVES

Since the onset of the COVID-19 pandemic in 2020, there has been a significant decline in seasonal influenza infection cases in Hong Kong. However, this decline has also resulted in reduced opportunities for the development of influenza-specific antibodies in the community. The levels of antibodies required for protection against recently circulating influenza A viruses in the post-COVID-19 era remain unclear.

METHODS

This study involved the analysis of paired plasma samples collected from 479 healthy adults in Hong Kong in 2021 and 2022. The neutralizing titers of plasma against influenza A (H1N1) and (H3N2) viruses circulating before and after the COVID-19 outbreak were determined using a microneutralization assay.

RESULTS

The H1N1 and H3N2 vaccine strains selected for the 2022/23 season were found to be closely related to the recently circulating viruses. However, in the samples collected in 2022, only 14.61% and 0.42% showed a neutralization titer (MN50) ≥1:20 against H1N1 A/Wisconsin/588/2019 (H1/Wis19) and H3N2 A/Darwin/6/2021 (H3/Dar21), respectively. Notably, participants who reported receiving annual flu vaccinations exhibited a higher seropositive rate for H1/Wis19 compared to those who had never received the flu vaccine (28.06% vs. 5.30%).

CONCLUSION

Our results indicate that adults in Hong Kong generally lack neutralizing antibodies against circulating influenza A viruses, particularly H3N2. These findings underscore the importance of promoting flu vaccination in the post-COVID-19 era.

Mutual information networks reveal evolutionary relationships within the influenza A virus polymerase

The influenza A virus (IAV) RNA polymerase is an essential driver of IAV evolution. Mutations that the polymerase introduces into viral genome segments during replication are the ultimate source of genetic variation, including within the three subunits of the IAV polymerase (polymerase basic protein 2, polymerase basic protein 1, and polymerase acidic protein). Evolutionary analysis of the IAV polymerase is complicated, because changes in mutation rate, replication speed, and drug resistance involve epistatic interactions among its subunits. In order to study the evolution of the human seasonal H3N2 polymerase since the 1968 pandemic, we identified pairwise evolutionary relationships among ∼7000 H3N2 polymerase sequences using mutual information (MI), which measures the information gained about the identity of one residue when a second residue is known. To account for uneven sampling of viral sequences over time, we developed a weighted MI (wMI) metric and demonstrate that wMI outperforms raw MI through simulations using a well-sampled severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) dataset. We then constructed wMI networks of the H3N2 polymerase to extend the inherently pairwise wMI statistic to encompass relationships among larger groups of residues. We included hemagglutinin (HA) in the wMI network to distinguish between functional wMI relationships within the polymerase and those potentially due to hitch-hiking on antigenic changes in HA. The wMI networks reveal coevolutionary relationships among residues with roles in replication and encapsidation. Inclusion of HA highlighted polymerase-only subgraphs containing residues with roles in the enzymatic functions of the polymerase and host adaptability. This work provides insight into the factors that drive and constrain the rapid evolution of influenza viruses.

Mutual information networks reveal evolutionary relationships within the influenza A virus polymerase

The influenza A (IAV) RNA polymerase is an essential driver of IAV evolution. Mutations that the polymerase introduces into viral genome segments during replication are the ultimate source of genetic variation, including within the three subunits of the IAV polymerase (PB2, PB1, and PA). Evolutionary analysis of the IAV polymerase is complicated, because changes in mutation rate, replication speed, and drug resistance involve epistatic interactions among its subunits. In order to study the evolution of the human seasonal H3N2 polymerase since the 1968 pandemic, we identified pairwise evolutionary relationships among ∼7000 H3N2 polymerase sequences using mutual information (MI), which measures the information gained about the identity of one residue when a second residue is known. To account for uneven sampling of viral sequences over time, we developed a weighted MI metric (wMI) and demonstrate that wMI outperforms raw MI through simulations using a well-sampled SARS-CoV-2 dataset. We then constructed wMI networks of the H3N2 polymerase to extend the inherently pairwise wMI statistic to encompass relationships among larger groups of residues. We included HA in the wMI network to distinguish between functional wMI relationships within the polymerase and those potentially due to hitchhiking on antigenic changes in HA. The wMI networks reveal coevolutionary relationships among residues with roles in replication and encapsidation. Inclusion of HA highlighted polymerase-only subgraphs containing residues with roles in the enzymatic functions of the polymerase and host adaptability. This work provides insight into the factors that drive and constrain the rapid evolution of influenza viruses.

Contributions of adaptation and purifying selection to SARS-CoV-2 evolution

Continued evolution and adaptation of SARS-CoV-2 has led to more transmissible and immune-evasive variants with profound impacts on the course of the pandemic. Here I analyze the evolution of the virus over 2.5 years since its emergence and estimate the rates of evolution for synonymous and non-synonymous changes separately for evolution within clades-well-defined monophyletic groups with gradual evolution-and for the pandemic overall. The rate of synonymous mutation is found to be around 6 changes per year. Synonymous rates within variants vary little from variant to variant and are compatible with the overall rate of 7 changes per year (or [Formula: see text] per year and codon). In contrast, the rate at which variants accumulate amino acid changes (non-synonymous mutations) was initially around 12-16 changes per year, but in 2021 and 2022 it dropped to 6-9 changes per year. The overall rate of non-synonymous evolution, that is across variants, is estimated to be about 26 amino acid changes per year (or [Formula: see text] per year and codon). This strong acceleration of the overall rate compared to within clade evolution indicates that the evolutionary process that gave rise to the different variants is qualitatively different from that in typical transmission chains and likely dominated by adaptive evolution. I further quantify the spectrum of mutations and purifying selection in different SARS-CoV-2 proteins and show that the massive global sampling of SARS-CoV-2 is sufficient to estimate site-specific fitness costs across the entire genome. Many accessory proteins evolve under limited evolutionary constraints with little short-term purifying selection. About half of the mutations in other proteins are strongly deleterious.

Discovering recent selection forces shaping the evolution of dengue viruses based on polymorphism data across geographic scales

Incomplete selection makes it challenging to infer selection on genes at short time scales, especially for microorganisms, due to stronger linkage between loci. However, in many cases, the selective force changes with environment, time, or other factors, and it is of great interest to understand selective forces at this level to answer relevant biological questions. We developed a new method that uses the change in d_N /d_S , instead of the absolute value of d_N /d_S , to infer the dominating selective force based on sequence data across geographical scales. If a gene was under positive selection, d_N /d_S was expected to increase through time, whereas if a gene was under negative selection, d_N /d_S was expected to decrease through time. Assuming that the migration rate decreased and the divergence time between samples increased from between-continent, within-continent different-country, to within-country level, d_N /d_S of a gene dominated by positive selection was expected to increase with increasing geographical scales, and the opposite trend was expected in the case of negative selection. Motivated by the McDonald-Kreitman (MK) test, we developed a pairwise MK test to assess the statistical significance of detected trends in d_N /d_S . Application of the method to a global sample of dengue virus genomes identified multiple significant signatures of selection in both the structural and non-structural proteins. Because this method does not require allele frequency estimates and uses synonymous mutations for comparison, it is less prone to sampling error, providing a way to infer selection forces within species using publicly available genomic data from locations over broad geographical scales.

A Bayesian approach to infer recombination patterns in coronaviruses

As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent.

Interactions between Influenza A Virus Nucleoprotein and Gene Segment Untranslated Regions Facilitate Selective Modulation of Viral Gene Expression

The influenza A virus (IAV) genome is divided into eight negative-sense, single-stranded RNA segments. Each segment exhibits a unique level and temporal pattern of expression; however, the exact mechanisms underlying the patterns of individual gene segment expression are poorly understood. We previously demonstrated that a single substitution in the viral nucleoprotein (NP:F346S) selectively modulates neuraminidase (NA) gene segment expression while leaving other segments largely unaffected. Given what is currently known about NP function, there is no obvious explanation for how changes in NP can selectively modulate the replication of individual gene segments. In this study, we found that the specificity of this effect for the NA segment is virus strain specific and depends on the untranslated region (UTR) sequences of the NA segment. While the NP:F346S substitution did not significantly alter the RNA binding or oligomerization activities of NP in vitro, it specifically decreased the ability of NP to promote NA segment viral RNA (vRNA) synthesis. In addition to NP residue F346, we identified two other adjacent aromatic residues in NP (Y385 and F479) capable of similarly regulating NA gene segment expression, suggesting a larger role for this domain in gene-segment specific regulation. Our findings reveal a novel role for NP in selective regulation of viral gene segment replication and provide a framework for understanding how the expression patterns of individual viral gene segments can be modulated during adaptation to new host environments. IMPORTANCE Influenza A virus (IAV) is a respiratory pathogen that remains a significant source of morbidity and mortality. Escape from host immunity or emergence into new host species often requires mutations that modulate the functional activities of the viral glycoproteins hemagglutinin (HA) and neuraminidase (NA), which are responsible for virus attachment to and release from host cells, respectively. Maintaining the functional balance between the activities of HA and NA is required for fitness across multiple host systems. Thus, selective modulation of viral gene expression patterns may be a key determinant of viral immune escape and cross-species transmission potential. We identified a novel mechanism by which the viral nucleoprotein (NP) gene can selectively modulate NA segment replication and gene expression through interactions with the segment UTRs. Our work highlights an unexpected role for NP in selective regulation of expression from the individual IAV gene segments.

Antigenic characterization of influenza and SARS-CoV-2 viruses

Antigenic characterization of emerging and re-emerging viruses is necessary for the prevention of and response to outbreaks, evaluation of infection mechanisms, understanding of virus evolution, and selection of strains for vaccine development. Primary analytic methods, including enzyme-linked immunosorbent/lectin assays, hemagglutination inhibition, neuraminidase inhibition, micro-neutralization assays, and antigenic cartography, have been widely used in the field of influenza research. These techniques have been improved upon over time for increased analytical capacity, and some have been mobilized for the rapid characterization of the SARS-CoV-2 virus as well as its variants, facilitating the development of highly effective vaccines within 1 year of the initially reported outbreak. While great strides have been made for evaluating the antigenic properties of these viruses, multiple challenges prevent efficient vaccine strain selection and accurate assessment. For influenza, these barriers include the requirement for a large virus quantity to perform the assays, more than what can typically be provided by the clinical samples alone, cell- or egg-adapted mutations that can cause antigenic mismatch between the vaccine strain and circulating viruses, and up to a 6-month duration of vaccine development after vaccine strain selection, which allows viruses to continue evolving with potential for antigenic drift and, thus, antigenic mismatch between the vaccine strain and the emerging epidemic strain. SARS-CoV-2 characterization has faced similar challenges with the additional barrier of the need for facilities with high biosafety levels due to its infectious nature. In this study, we review the primary analytic methods used for antigenic characterization of influenza and SARS-CoV-2 and discuss the barriers of these methods and current developments for addressing these challenges.

Effects of Host and Pathogenicity on Mutation Rates in Avian Influenza A Viruses

Mutation is the primary determinant of genetic diversity in influenza viruses. The rate of mutation, measured in an absolute time-scale, is likely to be dependent on the rate of errors in copying RNA sequences per replication and the number of replications per unit time. Conditions for viral replication are probably different among host taxa, potentially generating the host-specificity of the viral mutation rate, and possibly between highly and low pathogenic viruses. This study investigated whether mutation rates per year in avian influenza A viruses depend on host taxa and pathogenicity. We inferred mutation rates from the rates of synonymous substitutions, which are assumed to be neutral and thus equal to mutation rates, at four segments that code internal viral proteins (PB2, PB1, PA, NP). On the phylogeny of all avian viral sequences for each segment, multiple distinct subtrees (clades) were identified that represent viral subpopulations, which are likely to have evolved within particular host taxa. Using simple regression analysis, we found that mutation rates were significantly higher in viruses infecting chickens than domestic ducks, and in those infecting wild shorebirds than wild ducks. Host-dependency of the substitution rate was also confirmed by Bayesian phylogenetic analysis. However, we did not find evidence that the mutation rate is higher in highly pathogenic than in low pathogenic viruses. We discuss these results considering viral replication rate as the major determinant of mutation rate per unit time.

Recombination patterns in coronaviruses

As shown during the SARS-CoV-2 pandemic, phylogenetic and phylodynamic methods are essential tools to study the spread and evolution of pathogens. One of the central assumptions of these methods is that the shared history of pathogens isolated from different hosts can be described by a branching phylogenetic tree. Recombination breaks this assumption. This makes it problematic to apply phylogenetic methods to study recombining pathogens, including, for example, coronaviruses. Here, we introduce a Markov chain Monte Carlo approach that allows inference of recombination networks from genetic sequence data under a template switching model of recombination. Using this method, we first show that recombination is extremely common in the evolutionary history of SARS-like coronaviruses. We then show how recombination rates across the genome of the human seasonal coronaviruses 229E, OC43 and NL63 vary with rates of adaptation. This suggests that recombination could be beneficial to fitness of human seasonal coronaviruses. Additionally, this work sets the stage for Bayesian phylogenetic tracking of the spread and evolution of SARS-CoV-2 in the future, even as recombinant viruses become prevalent.

Purifying Selection Determines the Short-Term Time Dependency of Evolutionary Rates in SARS-CoV-2 and pH1N1 Influenza

High-throughput sequencing enables rapid genome sequencing during infectious disease outbreaks and provides an opportunity to quantify the evolutionary dynamics of pathogens in near real-time. One difficulty of undertaking evolutionary analyses over short timescales is the dependency of the inferred evolutionary parameters on the timespan of observation. Crucially, there are an increasing number of molecular clock analyses using external evolutionary rate priors to infer evolutionary parameters. However, it is not clear which rate prior is appropriate for a given time window of observation due to the time-dependent nature of evolutionary rate estimates. Here, we characterize the molecular evolutionary dynamics of SARS-CoV-2 and 2009 pandemic H1N1 (pH1N1) influenza during the first 12 months of their respective pandemics. We use Bayesian phylogenetic methods to estimate the dates of emergence, evolutionary rates, and growth rates of SARS-CoV-2 and pH1N1 over time and investigate how varying sampling window and data set sizes affect the accuracy of parameter estimation. We further use a generalized McDonald-Kreitman test to estimate the number of segregating nonneutral sites over time. We find that the inferred evolutionary parameters for both pandemics are time dependent, and that the inferred rates of SARS-CoV-2 and pH1N1 decline by ∼50% and ∼100%, respectively, over the course of 1 year. After at least 4 months since the start of sequence sampling, inferred growth rates and emergence dates remain relatively stable and can be inferred reliably using a logistic growth coalescent model. We show that the time dependency of the mean substitution rate is due to elevated substitution rates at terminal branches which are 2-4 times higher than those of internal branches for both viruses. The elevated rate at terminal branches is strongly correlated with an increasing number of segregating nonneutral sites, demonstrating the role of purifying selection in generating the time dependency of evolutionary parameters during pandemics.

Elucidating T Cell and B Cell Responses to SARS-CoV-2 in Humans: Gaining Insights into Protective Immunity and Immunopathology

The SARS-CoV-2 pandemic is an unprecedented epochal event on at least two fronts. Firstly, in terms of the rapid spread and the magnitude of the outbreak, and secondly, on account of the equally swift response of the scientific community that has galvanized itself into action and has successfully developed, tested and deployed highly effective and novel vaccines in record time to combat the virus. The sophistication and diversification of the scientific toolbox we now have at our disposal has enabled us to interrogate both the breadth and the depth of the immune response to a degree that is unparalleled in recent memory. In terms of our understanding of what is critical to contain the virus and mitigate the effects the pandemic, neutralizing antibodies to SARS-CoV-2 garner most of the attention, however, it is essential to recognize that it is the quality and the fitness of the virus-specific T cell and B cell response that lays the foundation and the backdrop for an effective neutralizing antibody response. In this report, we will review some of the key findings that have helped define and delineate some of the essential attributes of T and B cell responses in the setting of SARS-CoV-2 infection.

Complete Genome Sequence, Molecular Characterization and Phylogenetic Relationships of a Novel Tern Atadenovirus

Discovery and study of viruses carried by migratory birds are tasks of high importance due to the host's ability to spread infectious diseases over significant distances. With this paper, we present and characterize the first complete genome sequence of atadenovirus from a tern bird (common tern, Sterna hirundo) preliminarily named tern atadenovirus 1 (TeAdV-1). TeAdV-1 genome is a linear double-stranded DNA molecule, 31,334 base pairs which contain 30 methionine-initiated open reading frames with gene structure typical for Atadenovirus genus, and the shortest known inverted terminal repeats (ITRs) within the Atadenovirus genus consisted of 25 bases. The nucleotide composition of the genome is characterized by a low G + C content (33.86%), which is the most AT-rich genome of known avian adenoviruses within Atadenovirus genus. The nucleotide sequence of the TeAdV-1 genome shows high divergence compared to known representatives of the Atadenovirus genus with the highest similarity to the duck atadenovirus 1 (53.7%). Phylogenetic analysis of the protein sequences of core genes confirms the taxonomic affiliation of the new representative to the genus Atadenovirus with the degree of divergence from the known representatives exceeding the interspecies distance within the genus. Thereby we proposed a novel TeAdV-1 to be considered as a separate species.

Influenza Neuraminidase Characteristics and Potential as a Vaccine Target

Neuraminidase of influenza A and B viruses plays a critical role in the virus life cycle and is an important target of the host immune system. Here, we highlight the current understanding of influenza neuraminidase structure, function, antigenicity, immunogenicity, and immune protective potential. Neuraminidase inhibiting antibodies have been recognized as correlates of protection against disease caused by natural or experimental influenza A virus infection in humans. In the past years, we have witnessed an increasing interest in the use of influenza neuraminidase to improve the protective potential of currently used influenza vaccines. A number of well-characterized influenza neuraminidase-specific monoclonal antibodies have been described recently, most of which can protect in experimental challenge models by inhibiting the neuraminidase activity or by Fc receptor-dependent mechanisms. The relative instability of the neuraminidase poses a challenge for protein-based antigen design. We critically review the different solutions that have been proposed to solve this problem, ranging from the inclusion of stabilizing heterologous tetramerizing zippers to the introduction of inter-protomer stabilizing mutations. Computationally engineered neuraminidase antigens have been generated that offer broad, within subtype protection in animal challenge models. We also provide an overview of modern vaccine technology platforms that are compatible with the induction of robust neuraminidase-specific immune responses. In the near future, we will likely see the implementation of influenza vaccines that confront the influenza virus with a double punch: targeting both the hemagglutinin and the neuraminidase.

Inference of population genetic parameters from an irregular time series of seasonal influenza virus sequences

Basic summary statistics that quantify the population genetic structure of influenza virus are important for understanding and inferring the evolutionary and epidemiological processes. However, the sampling dates of global virus sequences in the last several decades are scattered nonuniformly throughout the calendar. Such temporal structure of samples and the small effective size of viral population hampers the use of conventional methods to calculate summary statistics. Here, we define statistics that overcome this problem by correcting for the sampling-time difference in quantifying a pairwise sequence difference. A simple linear regression method jointly estimates the mutation rate and the level of sequence polymorphism, thus providing an estimate of the effective population size. It also leads to the definition of Wright's FST for arbitrary time-series data. Furthermore, as an alternative to Tajima's D statistic or the site-frequency spectrum, a mismatch distribution corrected for sampling-time differences can be obtained and compared between actual and simulated data. Application of these methods to seasonal influenza A/H3N2 viruses sampled between 1980 and 2017 and sequences simulated under the model of recurrent positive selection with metapopulation dynamics allowed us to estimate the synonymous mutation rate and find parameter values for selection and demographic structure that fit the observation. We found that the mutation rates of HA and PB1 segments before 2007 were particularly high and that including recurrent positive selection in our model was essential for the genealogical structure of the HA segment. Methods developed here can be generally applied to population genetic inferences using serially sampled genetic data.

Structure-Guided Creation of an Anti-HA Stalk Antibody F11 Derivative That Neutralizes Both F11-Sensitive and -Resistant Influenza A(H1N1)pdm09 Viruses

The stalk domain of influenza virus envelope glycoprotein hemagglutinin (HA) constitutes the axis connecting the head and transmembrane domains, and plays pivotal roles in conformational rearrangements of HA for virus infection. Here we characterized molecular interactions between the anti-HA stalk neutralization antibody F11 and influenza A(H1N1)pdm09 HA to understand the structural basis of the actions and modifications of this antibody. In silico structural analyses using a model of the trimeric HA ectodomain indicated that the F11 Fab fragment has physicochemical properties, allowing it to crosslink two HA monomers by binding to a region near the proteolytic cleavage site of the stalk domain. Interestingly, the F11 binding allosterically caused a marked suppression of the structural dynamics of the HA cleavage loop and flanking regions. Structure-guided mutagenesis of the F11 antibody revealed a critical residue in the F11 light chain for the F11-mediated neutralization. Finally, the mutagenesis led to identification of a unique F11 derivative that can neutralize both F11-sensitive and F11-resistant A(H1N1)pdm09 viruses. These results raise the possibility that F11 sterically and physically disturbs proteolytic cleavage of HA for the ordered conformational rearrangements and suggest that in silico guiding experiments can be useful to create anti-HA stalk antibodies with new phenotypes.

Repeated influenza vaccination provides cumulative protection from distinct H3N2 viruses

OBJECTIVES

Current inactivated influenza vaccines provide suboptimal protection against antigenic drift, and repeated annual vaccinations shape antibody specificity but the effect on protection from infection is not well understood.

METHODS

We studied the effects of cumulative and staggered vaccinations in mice to determine the effect of influenza vaccination on protection from infection and immune quality.

RESULTS

We found that the timing of vaccination and antigenic change impacted the quality of immune responses. When mice received two different H3N2 strains (A/Hong Kong/4801/2014 and A/Singapore/INFIMH-16-0019/2016) by staggered timing of vaccination, there were higher H3HA antibody and B-cell memory responses than four cumulative vaccinations or when two vaccinations were successive. Interestingly, after challenge with a lethal-drifted H3N2 virus (A/Hong Kong/1/1968), mice with staggered vaccination were unable to produce high titres of antibodies specific to the challenge strain compared to other vaccination regimens because of high levels of vaccine-specific cross-reactive antibodies. All vaccination regimens resulted in protection, in terms of viral loads and survival, from lethal challenge, while lung IL-6 and inflammation were lowest in staggered or cumulative vaccination groups, indicating further advantage.

CONCLUSION

Our findings help justify influenza vaccination policies that currently recommend repeat vaccination in infants and annual seasonal vaccination, with no evidence for impaired immunity by repeated seasonal vaccination.

Evaluation of HA-D222G/N polymorphism using targeted NGS analysis in A(H1N1)pdm09 influenza virus in Russia in 2018-2019

Outbreaks of influenza, which is a contagious respiratory disease, occur throughout the world annually, affecting millions of people with many fatal cases. The D222G/N mutations in the hemagglutinin (HA) gene of A(H1N1)pdm09 are associated with severe and fatal human influenza cases. These mutations lead to increased virus replication in the lower respiratory tract (LRT) and may result in life-threatening pneumonia. Targeted NGS analysis revealed the presence of mutations in major and minor variants in 57% of fatal cases, with the proportion of viral variants with mutations varying from 1% to 98% in each individual sample in the epidemic season 2018-2019 in Russia. Co-occurrence of the mutations D222G and D222N was detected in a substantial number of the studied fatal cases (41%). The D222G/N mutations were detected at a low frequency (less than 1%) in the rest of the studied samples from fatal and nonfatal cases of influenza. The presence of HA D222Y/V/A mutations was detected in a few fatal cases. The high rate of occurrence of HA D222G/N mutations in A(H1N1)pdm09 viruses, their increased ability to replicate in the LRT and their association with fatal outcomes points to the importance of monitoring the mutations in circulating A(H1N1)pdm09 viruses for the evaluation of their epidemiological significance and for the consideration of disease prevention and treatment options.

Strategies Targeting Hemagglutinin as a Universal Influenza Vaccine

Influenza virus has significant viral diversity, both through antigenic drift and shift, which makes development of a vaccine challenging. Current influenza vaccines are updated yearly to include strains predicted to circulate in the upcoming influenza season, however this can lead to a mismatch which reduces vaccine efficacy. Several strategies targeting the most abundant and immunogenic surface protein of influenza, the hemagglutinin (HA) protein, have been explored. These strategies include stalk-directed, consensus-based, and computationally derived HA immunogens. In this review, we explore vaccine strategies which utilize novel antigen design of the HA protein to improve cross-reactive immunity for development of a universal influenza vaccine.

Limited Predictability of Amino Acid Substitutions in Seasonal Influenza Viruses

Seasonal influenza viruses repeatedly infect humans in part because they rapidly change their antigenic properties and evade host immune responses, necessitating frequent updates of the vaccine composition. Accurate predictions of strains circulating in the future could therefore improve the vaccine match. Here, we studied the predictability of frequency dynamics and fixation of amino acid substitutions. Current frequency was the strongest predictor of eventual fixation, as expected in neutral evolution. Other properties, such as occurrence in previously characterized epitopes or high Local Branching Index (LBI) had little predictive power. Parallel evolution was found to be moderately predictive of fixation. Although the LBI had little power to predict frequency dynamics, it was still successful at picking strains representative of future populations. The latter is due to a tendency of the LBI to be high for consensus-like sequences that are closer to the future than the average sequence. Simulations of models of adapting populations, in contrast, show clear signals of predictability. This indicates that the evolution of influenza HA and NA, while driven by strong selection pressure to change, is poorly described by common models of directional selection such as traveling fitness waves.

Immunogenicity of standard, high-dose, MF59-adjuvanted, and recombinant-HA seasonal influenza vaccination in older adults

The vaccine efficacy of standard-dose seasonal inactivated influenza vaccines (S-IIV) can be improved by the use of vaccines with higher antigen content or adjuvants. We conducted a randomized controlled trial in older adults to compare cellular and antibody responses of S-IIV versus enhanced vaccines (eIIV): MF59-adjuvanted (A-eIIV), high-dose (H-eIIV), and recombinant-hemagglutinin (HA) (R-eIIV). All vaccines induced comparable H3-HA-specific IgG and elevated antibody-dependent cellular cytotoxicity (ADCC) activity at day 30 post vaccination. H3-HA-specific ADCC responses were greatest following H-eIIV. Only A-eIIV increased H3-HA-IgG avidity, HA-stalk IgG and ADCC activity. eIIVs also increased polyfunctional CD4+ and CD8+ T cell responses, while cellular immune responses were skewed toward single-cytokine-producing T cells among S-IIV subjects. Our study provides further immunological evidence for the preferential use of eIIVs in older adults as each vaccine platform had an advantage over the standard-dose vaccine in terms of NK cell activation, HA-stalk antibodies, and T cell responses.

Evidence for adaptive evolution in the receptor-binding domain of seasonal coronaviruses OC43 and 229e

Seasonal coronaviruses (OC43, 229E, NL63, and HKU1) are endemic to the human population, regularly infecting and reinfecting humans while typically causing asymptomatic to mild respiratory infections. It is not known to what extent reinfection by these viruses is due to waning immune memory or antigenic drift of the viruses. Here we address the influence of antigenic drift on immune evasion of seasonal coronaviruses. We provide evidence that at least two of these viruses, OC43 and 229E, are undergoing adaptive evolution in regions of the viral spike protein that are exposed to human humoral immunity. This suggests that reinfection may be due, in part, to positively selected genetic changes in these viruses that enable them to escape recognition by the immune system. It is possible that, as with seasonal influenza, these adaptive changes in antigenic regions of the virus would necessitate continual reformulation of a vaccine made against them.

Influenza A H1 and H3 Transmembrane Domains Interact Differently with Each Other and with Surrounding Membrane Lipids

Hemagglutinin (HA) is a class I viral membrane fusion protein, which is the most abundant transmembrane protein on the surface of influenza A virus (IAV) particles. HA plays a crucial role in the recognition of the host cell, fusion of the viral envelope and the host cell membrane, and is the major antigen in the immune response during the infection. Mature HA organizes in homotrimers consisting of a sequentially highly variable globular head and a relatively conserved stalk region. Every HA monomer comprises a hydrophilic ectodomain, a pre-transmembrane domain (pre-TMD), a hydrophobic transmembrane domain (TMD), and a cytoplasmic tail (CT). In recent years the effect of the pre-TMD and TMD on the structure and function of HA has drawn some attention. Using bioinformatic tools we analyzed all available full-length amino acid sequences of HA from 16 subtypes across various host species. We calculated several physico-chemical parameters of HA pre-TMDs and TMDs including accessible surface area (ASA), average hydrophobicity (H_av), and the hydrophobic moment (µ_H). Our data suggests that distinct differences in these parameters between the two major phylogenetic groups, represented by H1 and H3 subtypes, could have profound effects on protein–lipid interactions, trimer formation, and the overall HA ectodomain orientation and antigen exposure.

Evolution of H9N2 avian influenza viruses in Iran, 2017-2019

Since its first detection in 1998, avian influenza virus (AIV) subtype H9N2 has been enzootic in Iran. To better understand the evolutionary history of H9N2 viruses in Iran, we sequenced 15 currently circulating H9N2 viruses from domestic poultry during 2017-2019 and performed phylogenetic analysis of complete genome sequences. Phylogenetic analyses indicated that the Iranian H9N2 viruses formed multiple well-supported monophyletic groups within the G1-lineage of H9N2 virus. Our analysis of viral population dynamics revealed an increase in genetic diversity until 2007, corresponding to the multiple introductions and diversification of H9N2 viruses into multiple genetic groups (named Iran 1-4 subgroups), followed by a sudden decrease after 2008. Only the Iran 4 subgroup has survived, expanded, and currently circulates in Iran. The H9N2 viruses possessed many molecular markers associated with mammalian adaption in all gene segments, except neuraminidase gene. Considering the presence of mammalian host-specific markers, the public health threat of H9N2 viruses continues. Molecular analysis showed that Iranian H9N2 strains have continued to evolve and recent strains have multiple amino acid changes and addition of potential N-glycosylation on the antigenic sites of haemagglutinin. Continued antigenic and molecular surveillance of H9N2 viruses in poultry and mammals would be required to monitor further increments in viral evolution and their potential threat to public health.

Influenza-Host Interplay and Strategies for Universal Vaccine Development

Influenza is an annual epidemic and an occasional pandemic caused by pathogens that are responsible for infectious respiratory disease. Humans are highly susceptible to the infection mediated by influenza A viruses (IAV). The entry of the virus is mediated by the influenza virus hemagglutinin (HA) glycoprotein that binds to the cellular sialic acid receptors and facilitates the fusion of the viral membrane with the endosomal membrane. During IAV infection, virus-derived pathogen-associated molecular patterns (PAMPs) are recognized by host intracellular specific sensors including toll-like receptors (TLRs), C-type lectin receptors, retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs), and nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) either on the cell surface or intracellularly in endosomes. Herein, we comprehensively review the current knowledge available on the entry of the influenza virus into host cells and the molecular details of the influenza virus–host interface. We also highlight certain strategies for the development of universal influenza vaccines.

Insights from the comparison of genomic variants from two influenza B viruses grown in the presence of human antibodies in cell culture

Understanding the extent and limitation of viral genome evolution can provide insight about potential drug and vaccine targets. Influenza B Viruses (IBVs) infect humans in a seasonal manner and causes significant morbidity and mortality. IBVs are negative-sense single-stranded RNA viruses with a segmented genome and can be divided into two antigenically distinct lineages. The two lineages have been circulating and further evolving for almost four decades. The immune response to IBV infection can lead to antibodies that target the strain causing the infection. Some antibodies are cross-reactive and are able to bind strains from both lineages but, because of antigenic drift and immunodominance, both lineages continue to evolve and challenge human health. Here we investigate changes in the genomes of an IBVs from each lineage after passage in tissue culture in the presence of human sera containing polyclonal antibodies directed toward antigenically and temporally distinct viruses. Our previous analysis of the fourth segment, which encodes the major surface protein HA, revealed a pattern of change in which signature sequences from one lineage mutated to the signature sequences of the other lineage. Here we analyze genes from the other genomic segments and observe that most of the quasispecies’ heterogeneity occurs at the same loci in each lineage. The nature of the variants at these loci are investigated and possible reasons for this pattern are discussed. This work expands our understanding of the extent and limitations of genomic change in IBV.

Early prediction of antigenic transitions for influenza A/H3N2

Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.

The efficacy of annual seasonal influenza vaccines depends on selecting the strain that best matches circulating viruses. This selection takes place 9–12 months prior to the influenza season. To advise this decision, we used an influenza A/H3N2 phylodynamic simulation to explore how reliably and how far in advance can we identify strains that will dominate future influenza seasons? What data should we collect to accelerate and improve the accuracy of such forecasts? And importantly, what is the gap between the theoretical limit of prediction and prediction based on current influenza surveillance? Our results suggest that even with detailed virological information, the tight race between the antigenic turnover dynamics and the vaccine development timeline limits early detection of emerging viruses. Predictions based on current influenza surveillance do not achieve the theoretical limit and thus our results provide impetus for denser sampling and the development of rapid methods for estimating viral fitness.

Linking influenza virus evolution within and between human hosts

Influenza viruses rapidly diversify within individual human infections. Several recent studies have deep-sequenced clinical influenza infections to identify viral variation within hosts, but it remains unclear how within-host mutations fare at the between-host scale. Here, we compare the genetic variation of H3N2 influenza within and between hosts to link viral evolutionary dynamics across scales. Synonymous sites evolve at similar rates at both scales, indicating that global evolution at these putatively neutral sites results from the accumulation of within-host variation. However, nonsynonymous mutations are depleted between hosts compared to within hosts, suggesting that selection purges many of the protein-altering changes that arise within hosts. The exception is at antigenic sites, where selection detectably favors nonsynonymous mutations at the global scale, but not within hosts. These results suggest that selection against deleterious mutations and selection for antigenic change are the main forces that act on within-host variants of influenza virus as they transmit and circulate between hosts.

Evolution and rapid spread of a reassortant A(H3N2) virus that predominated the 2017-2018 influenza season

The 2017-2018 North American influenza season caused more hospitalizations and deaths than any year since the 2009 H1N1 pandemic. The majority of recorded influenza infections were caused by A(H3N2) viruses, with most of the virus's North American diversity falling into the A2 clade. Within A2, we observe a subclade which we call A2/re that rose to comprise almost 70 per cent of A(H3N2) viruses circulating in North America by early 2018. Unlike most fast-growing clades, however, A2/re contains no amino acid substitutions in the hemagglutinin (HA) segment. Moreover, hemagglutination inhibition assays did not suggest substantial antigenic differences between A2/re viruses and viruses sampled during the 2016-2017 season. Rather, we observe that the A2/re clade was the result of a reassortment event that occurred in late 2016 or early 2017 and involved the combination of the HA and PB1 segments of an A2 virus with neuraminidase (NA) and other segments a virus from the clade A1b. The success of this clade shows the need for antigenic analysis that targets NA in addition to HA. Our results illustrate the potential for non-HA drivers of viral success and necessitate the need for more thorough tracking of full viral genomes to better understand the dynamics of influenza epidemics.

Global patterns of avian influenza A (H7): virus evolution and zoonotic threats

Avian influenza viruses (AIVs) continue to impose a negative impact on animal and human health worldwide. In particular, the emergence of highly pathogenic AIV H5 and, more recently, the emergence of low pathogenic AIV H7N9 have led to enormous socioeconomical losses in the poultry industry and resulted in fatal human infections. While H5N1 remains infamous, the number of zoonotic infections with H7N9 has far surpassed those attributed to H5. Despite the clear public health concerns posed by AIV H7, it is unclear why specifically this virus subtype became endemic in poultry and emerged in humans. In this review, we bring together data on global patterns of H7 circulation, evolution and emergence in humans. Specifically, we discuss data from the wild bird reservoir, expansion and epidemiology in poultry, significant increase in their zoonotic potential since 2013 and genesis of highly pathogenic H7. In addition, we analysed available sequence data from an evolutionary perspective, demonstrating patterns of introductions into distinct geographic regions and reassortment dynamics. The integration of all aspects is crucial in the optimisation of surveillance efforts in wild birds, poultry and humans, and we emphasise the need for a One Health approach in controlling emerging viruses such as AIV H7.

Allele-specific nonstationarity in evolution of influenza A virus surface proteins

Influenza A virus (IAV) is a major public health problem and a pandemic threat. Its evolution is largely driven by diversifying positive selection so that relative fitness of different amino acid variants changes with time due to changes in herd immunity or genomic context, and novel amino acid variants attain fitness advantage. Here, we hypothesize that diversifying selection also has another manifestation: the fitness associated with a particular amino acid variant should decline with time since its origin, as the herd immunity adapts to it. By tracing the evolution of antigenic sites at IAV surface proteins, we show that an amino acid variant becomes progressively more likely to become replaced by another variant with time since its origin-a phenomenon we call "senescence." Senescence is particularly pronounced at experimentally validated antigenic sites, implying that it is largely driven by host immunity. By contrast, at internal sites, existing variants become more favorable with time, probably due to arising contingent mutations at other epistatically interacting sites. Our findings reveal a previously undescribed facet of adaptive evolution and suggest approaches for prediction of evolutionary dynamics of pathogens.

Evolutionary, genetic, structural characterization and its functional implications for the influenza A (H1N1) infection outbreak in India from 2009 to 2017

Influenza A (H1N1) continues to be a major public health threat due to possible emergence of a more virulent H1N1 strain resulting from dynamic changes in virus adaptability consequent to functional mutations and antigenic drift in the hemagglutinin (HA) and neuraminidase (NA) surface proteins. In this study, we describe the genetic and evolutionary characteristics of H1N1 strains that circulated in India over a period of nine years from 2009 to 2017 in relation to global strains. The finding is important from a global perspective since previous phylogenetic studies have suggested that the tropics contributed substantially to the global circulation of influenza viruses. Bayesian phylogenic analysis of HA sequences along with global strains indicated that there is a temporal pattern of H1N1 evolution and clustering of Indian isolates with globally circulating strains. Interestingly, we observed four new amino acid substitutions (S179N, I233T, S181T and I312V) in the HA sequence of H1N1 strains isolated during 2017 and two (S181T and I312V) were found to be unique in Indian isolates. Structurally these two unique mutations could lead to altered glycan specificity of the HA gene. Similarly, sequence and structural analysis of NA domain revealed that the presence of K432E mutation in H1N1 strains isolated after 2015 from India and in global strains found to induce a major loop shift in the vicinity of the catalytic site. The findings presented here offer an insight as to how these acquired mutations could be associated to an improved adaptability of the virus for efficient human transmissibility.

Exploitation of glycosylation in enveloped virus pathobiology

Glycosylation is a ubiquitous post-translational modification responsible for a multitude of crucial biological roles. As obligate parasites, viruses exploit host-cell machinery to glycosylate their own proteins during replication. Viral envelope proteins from a variety of human pathogens including HIV-1, influenza virus, Lassa virus, SARS, Zika virus, dengue virus, and Ebola virus have evolved to be extensively glycosylated. These host-cell derived glycans facilitate diverse structural and functional roles during the viral life-cycle, ranging from immune evasion by glycan shielding to enhancement of immune cell infection. In this review, we highlight the imperative and auxiliary roles glycans play, and how specific oligosaccharide structures facilitate these functions during viral pathogenesis. We discuss the growing efforts to exploit viral glycobiology in the development of anti-viral vaccines and therapies.