Sweep Dynamics (SD) plots: Computational identification of selective sweeps to monitor the adaptation of influenza A viruses

Characterization of Conserved Evolution in H7N9 Avian Influenza Virus Prior Mass Vaccination

Vaccination is crucial for the prevention and mitigation of avian influenza infections in China. The inactivated H7N9 vaccine, when administered to poultry, significantly lowers the risk of infection among both poultry and humans, while also markedly decreasing the prevalence of H7N9 detections. Highly pathogenic (HP) H7N9 viruses occasionally appear, whereas their low pathogenicity (LP) counterparts have been scarcely detected since 2018. However, these contributing factors remain poorly understood. We conducted an exploratory investigation of the mechanics via the application of comprehensive bioinformatic approaches. We delineated the Yangtze River Delta (YRD) H7N9 lineage into 5 clades (YRD-A to E). Our findings highlight the emergence and peak occurrence of the LP H7N9-containing YRD-E clade during the 5th epidemic wave in China's primary poultry farming areas. A more effective control of LP H7N9 through vaccination was observed compared to that of its HP H7N9 counterpart. YRD-E exhibited a tardy evolutionary trajectory, denoted by the conservation of its genetic and antigenic variation. Our analysis of YRD-E revealed only minimal amino acid substitutions along its phylogenetic tree and a few selective sweep mutations since 2016. In terms of epidemic fitness, the YRD-E was measured to be lower than that of the HP variants. Collectively, these findings underscore the conserved evolutionary patterns distinguishing the YRD-E. Given the conservation presented in its evolutionary patterns, the YRD-E LP H7N9 is hypothesized to be associated with a reduction following the mass vaccination in a relatively short period owing to its lower probability of antigenic variation that might affect vaccine efficiency.

The combined effect of oseltamivir and favipiravir on influenza a virus evolution in patients hospitalized with severe influenza

Our previous study shows favipiravir and oseltamivir combination therapy may accelerate clinical recovery compared to oseltamivir monotherapy in severe influenza, but its effect on virological evolution and resistance mutation against oseltamivir is still unknown. In this study, we collected longitudinal respiratory samples from influenza patients who underwent combination therapy and applied them to next generation sequencing of the whole genome of the influenza A virus (IAV). We also included a cohort untreated with any antivirals to serve as the control. In total, 62 samples from 19 patients treated with combination therapy and 20 samples from 20 patients untreated were successfully sequenced. The nucleotide diversity in the whole genome of IAV in the combination group showed no difference compared to that in the control group (P > 0.05). Moreover, we observed 174 kinds of nonsynonymous nucleotide substitutions in patients with combination therapy, mostly in NA (n = 44) and HA (n = 43). Of them, the G→A transition was the dominant variant type (27%) and 46/174 (26%) was reported to have biological effects, such as increased pathogenicity and polymerase activity. Among the 29 mutations conferring reduction in oseltamivir sensitivity we investigated, H275Y was the only mutation detected in the 4 samples from 1 of 19 patients and demonstrated increasing frequency during the treatment. Mutations conferring favipiravir resistance were not observed. Our studies showed combination therapy of favipiravir and oseltamivir has little effect on virus nucleotide diversity, nor prevents the increase of oseltamivir-resistant variants.

Evolution of the North American Lineage H7 Avian Influenza Viruses in Association with H7 Virus's Introduction to Poultry

The incursions of H7 subtype low-pathogenicity avian influenza virus (LPAIV) from wild birds into poultry and its mutations to highly pathogenic avian influenza virus (HPAIV) have been an ongoing concern in North America. Since 2000, 10 phylogenetically distinct H7 virus outbreaks from wild birds have been detected in poultry, six of which mutated to HPAIV. To study the molecular evolution of the H7 viruses that occurs when changing hosts from wild birds to poultry, we performed analyses of the North American H7 hemagglutinin (HA) genes to identify amino acid changes as the virus circulated in wild birds from 2000 to 2019. Then, we analyzed recurring HA amino acid changes and gene constellations of the viruses that spread from wild birds to poultry. We found six HA amino acid changes occurring during wild bird circulation and 10 recurring changes after the spread to poultry. Eight of the changes were in and around the HA antigenic sites, three of which were supported by positive selection. Viruses from each H7 outbreak had a unique genotype, with no specific genetic group associated with poultry outbreaks or mutation to HPAIV. However, the genotypes of the H7 viruses in poultry outbreaks tended to contain minor genetic groups less observed in wild bird H7 viruses, suggesting either a biased sampling of wild bird AIVs or a tendency of having reassortment with minor genetic groups prior to the virus's introduction to poultry. IMPORTANCE Wild bird-origin H7 subtype avian influenza viruses are a constant threat to commercial poultry, both directly by the disease they cause and indirectly through trade restrictions that can be imposed when the virus is detected in poultry. It is important to understand the genetic basis of why the North American lineage H7 viruses have repeatedly crossed the species barrier from wild birds to poultry. We examined the amino acid changes in the H7 viruses associated with poultry outbreaks and tried to determine gene reassortment related to poultry adaptation and mutations to HPAIV. The findings in this study increase the understanding of the evolutionary pathways of wild bird AIV before infecting poultry and the HA changes associated with adaptation of the virus in poultry.

Genetic and Antigenic Characterization of an Expanding H3 Influenza A Virus Clade in U.S. Swine Visualized by Nextstrain

Genetically distinct clades of influenza A virus (IAV) in swine undermine efforts to control the disease. Swine producers commonly use vaccines, and vaccine strains are selected by identifying the most common hemagglutinin (HA) gene from viruses detected in a farm or a region.

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.

Spatial and Temporal Coevolution of N2 Neuraminidase and H1 and H3 Hemagglutinin Genes of Influenza A Virus in United States Swine

The neuraminidase (NA) and hemagglutinin (HA) are essential surface glycoproteins of influenza A virus (IAV). In this study, the evolution of subtype N2 NA paired with H1 and H3 subtype HA in swine was evaluated to understand if genetic diversity of HA and NA were linked. Using time-scaled Bayesian phylodynamic analyses, the relationships of paired swine N2 with H1 or H3 from 2009 to 2018 were evaluated. These data demonstrated increased relative genetic diversity within the major N2 clades circulating in swine in the United States (N2.1998 between 2014-2017 and N2.2002 between 2010-2016). Preferential pairing was observed among specific NA and HA genetic clades. Gene reassortment between cocirculating influenza A strains resulted in novel pairings that persisted. The changes of genetic diversity in the NA gene were quantified using Bayesian phylodynamic analyses and increases in diversity were observed subsequent to novel NA-HA reassortment events. The rate of evolution among NA-N2 clades and HA-H1 and HA-H3 clades were similar. Bayesian phylodynamic analyses demonstrated strong spatial patterns in N2 genetic diversity, but frequent interstate movement of rare N2 clades provided opportunity for reassortment and emergence of new N2-HA pairings. The frequent regional movement of pigs and their influenza viruses is an explanation for the documented patterns of reassortment and subsequent changes in gene diversity. The reassortment and evolution of NA and linked HA evolution may result in antigenic drift of both major surface glycoproteins, reducing vaccine efficacy, with subsequent impact on animal health.

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.

Identifying Potentially Beneficial Genetic Mutations Associated with Monophyletic Selective Sweep and a Proof-of-Concept Study with Viral Genetic Data

Genetic mutations play a central role in evolution. For a significantly beneficial mutation, a one-time mutation event suffices for the species to prosper and predominate through the process called “monophyletic selective sweep.” However, existing methods that rely on counting the number of mutation events to detect selection are unable to find such a mutation in selective sweep. We here introduce a method to detect mutations at the single amino acid/nucleotide level that could be responsible for monophyletic selective sweep evolution. The method identifies a genetic signature associated with selective sweep using the population genetic test statistic Tajima’s D. We applied the algorithm to ebolavirus, influenza A virus, and severe acute respiratory syndrome coronavirus 2 to identify known biologically significant mutations and unrecognized mutations associated with potential selective sweep. The method can detect beneficial mutations, possibly leading to discovery of previously unknown biological functions and mechanisms related to those mutations.

IMPORTANCE In biology, research on evolution is important to understand the significance of genetic mutation. When there is a significantly beneficial mutation, a population of species with the mutation prospers and predominates, in a process called “selective sweep.” However, there are few methods that can find such a mutation causing selective sweep from genetic data. We here introduce a novel method to detect such mutations. Applying the method to the genomes of ebolavirus, influenza viruses, and the novel coronavirus, we detected known biologically significant mutations and identified mutations the importance of which is previously unrecognized. The method can deepen our understanding of molecular and evolutionary biology.

Low 2018/19 vaccine effectiveness against influenza A(H3N2) among 15-64-year-olds in Europe: exploration by birth cohort

Introduction

Influenza A(H3N2) clades 3C.2a and 3C.3a co-circulated in Europe in 2018/19. Immunological imprinting by first childhood influenza infection may induce future birth cohort differences in vaccine effectiveness (VE).

Aim

The I-MOVE multicentre primary care test-negative study assessed 2018/19 influenza A(H3N2) VE by age and genetic subgroups to explore VE by birth cohort.

Methods

We measured VE against influenza A(H3N2) and (sub)clades. We stratified VE by usual age groups (0–14, 15–64, ≥ 65-years). To assess the imprint-regulated effect of vaccine (I-REV) hypothesis, we further stratified the middle-aged group, notably including 32–54-year-olds (1964–86) sharing potential childhood imprinting to serine at haemagglutinin position 159.

Results

Influenza A(H3N2) VE among all ages was −1% (95% confidence interval (CI): −24 to 18) and 46% (95% CI: 8–68), −26% (95% CI: −66 to 4) and 20% (95% CI: −20 to 46) among 0–14, 15–64 and ≥ 65-year-olds, respectively. Among 15–64-year-olds, VE against clades 3C.2a1b and 3C.3a was 15% (95% CI: −34 to 50) and −74% (95% CI: −259 to 16), respectively. VE was −18% (95% CI: −140 to 41), −53% (95% CI: −131 to −2) and −12% (95% CI: −74 to 28) among 15–31-year-olds (1987–2003), 32–54-year-olds (1964–86) and 55–64-year-olds (1954–63), respectively.

Discussion

The lowest 2018/19 influenza A(H3N2) VE was against clade 3C.3a and among those born 1964–86, corresponding to the I-REV hypothesis. The low influenza A(H3N2) VE in 15–64-year-olds and the public health impact of the I-REV hypothesis warrant further study.

Computational Approaches and Challenges to Developing Universal Influenza Vaccines

The traditional design of effective vaccines for rapidly-evolving pathogens, such as influenza A virus, has failed to provide broad spectrum and long-lasting protection. With low cost whole genome sequencing technology and powerful computing capabilities, novel computational approaches have demonstrated the potential to facilitate the design of a universal influenza vaccine. However, few studies have integrated computational optimization in the design and discovery of new vaccines. Understanding the potential of computational vaccine design is necessary before these approaches can be implemented on a broad scale. This review summarizes some promising computational approaches under current development, including computationally optimized broadly reactive antigens with consensus sequences, phylogenetic model-based ancestral sequence reconstruction, and immunomics to compute conserved cross-reactive T-cell epitopes. Interactions between virus-host-environment determine the evolvability of the influenza population. We propose that with the development of novel technologies that allow the integration of data sources such as protein structural modeling, host antibody repertoire analysis and advanced phylodynamic modeling, computational approaches will be crucial for the development of a long-lasting universal influenza vaccine. Taken together, computational approaches are powerful and promising tools for the development of a universal influenza vaccine with durable and broad protection.

Structures and functions linked to genome-wide adaptation of human influenza A viruses

Human influenza A viruses elicit short-term respiratory infections with considerable mortality and morbidity. While H3N2 viruses circulate for more than 50 years, the recent introduction of pH1N1 viruses presents an excellent opportunity for a comparative analysis of the genome-wide evolutionary forces acting on both subtypes. Here, we inferred patches of sites relevant for adaptation, i.e. being under positive selection, on eleven viral protein structures, from all available data since 1968 and correlated these with known functional properties. Overall, pH1N1 have more patches than H3N2 viruses, especially in the viral polymerase complex, while antigenic evolution is more apparent for H3N2 viruses. In both subtypes, NS1 has the highest patch and patch site frequency, indicating that NS1-mediated viral attenuation of host inflammatory responses is a continuously intensifying process, elevated even in the longtime-circulating subtype H3N2. We confirmed the resistance-causing effects of two pH1N1 changes against oseltamivir in NA activity assays, demonstrating the value of the resource for discovering functionally relevant changes. Our results represent an atlas of protein regions and sites with links to host adaptation, antiviral drug resistance and immune evasion for both subtypes for further study.

Bioinformatics Meets Virology: The European Virus Bioinformatics Center's Second Annual Meeting

The Second Annual Meeting of the European Virus Bioinformatics Center (EVBC), held in Utrecht, Netherlands, focused on computational approaches in virology, with topics including (but not limited to) virus discovery, diagnostics, (meta-)genomics, modeling, epidemiology, molecular structure, evolution, and viral ecology. The goals of the Second Annual Meeting were threefold: (i) to bring together virologists and bioinformaticians from across the academic, industrial, professional, and training sectors to share best practice; (ii) to provide a meaningful and interactive scientific environment to promote discussion and collaboration between students, postdoctoral fellows, and both new and established investigators; (iii) to inspire and suggest new research directions and questions. Approximately 120 researchers from around the world attended the Second Annual Meeting of the EVBC this year, including 15 renowned international speakers. This report presents an overview of new developments and novel research findings that emerged during the meeting.