Integrating influenza antigenic dynamics with molecular evolution

Convolutional Neural Network Based Approach to in Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly important for vaccine production. The conventional method used to measure such a degree is related to performing the immunological assays of hemagglutinin inhibition. Namely, the antigenic distance between two strains is calculated on the basis of HI assays. Usually, such distances are visualized by using some kind of antigenic cartography method. The known drawback of the HI assay is that it is rather time-consuming and expensive. In this paper, we propose a novel approach for antigenic distance approximation based on deep learning in the feature spaces induced by hemagglutinin protein sequences and Convolutional Neural Networks (CNNs). To apply a CNN to compare the protein sequences, we utilize the encoding based on the physical and chemical characteristics of amino acids. By varying (hyper)parameters of the CNN architecture design, we find the most robust network. Further, we provide insight into the relationship between approximated antigenic distance and antigenicity by evaluating the network on the HI assay database for the H1N1 subtype. The results indicate that the best-trained network gives a high-precision approximation for the ground-truth antigenic distances, and can be used as a good exploratory tool in practical tasks.

Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution

Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence-only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth.

The impact of candidate influenza virus and egg-based manufacture on vaccine effectiveness: Literature review and expert consensus

INTRODUCTION

Influenza is associated with significant morbidity and mortality worldwide. Whilst vaccination is key for the prevention of influenza infection, there are many factors which may contribute to reduced vaccine effectiveness, including antigenic evolution via both antigenic drift and egg-adaptations. Due to the currently dissociated and indirect evidence supporting both the occurrence of these two phenomena in the egg-based manufacturing process and their effects on vaccine effectiveness, this topic remains a subject of debate.

OBJECTIVE

To review the evidence and level of agreement in expert opinion supporting a mechanistic basis for reduced vaccine effectiveness due to egg-based manufacturing, using an expert consensus-based methodology and literature reviews.

METHODS

Ten European influenza specialists were recruited to the expert panel. The overall research question was deconstructed into four component principles, which were examined in series using a novel, online, two-stage assessment of proportional group awareness and consensus. The first stage independently generated a list of supporting references for each component principle via literature searches and expert assessments. In the second stage, a summary of each reference was circulated amongst the experts, who rated their agreement that each reference supported the component principle on a 5-point Likert scale. Finally, the panel were asked if they agreed that, as a whole, the evidence supported a mechanistic basis for reduced vaccine effectiveness due to egg-based manufacturing.

RESULTS

All component principles were reported to have a majority of strong or very strong supporting evidence (70-90%).

CONCLUSIONS

On reviewing the evidence for all component principles, experts unanimously agreed that there is a mechanistic basis for reduced vaccine effectiveness resulting from candidate influenza virus variation due to egg-based manufacturing, particularly in the influenza A/H3N2 strain. Experts pointed to surveillance, candidate vaccine virus selection and manufacturing stages involving eggs as the most likely to impact vaccine effectiveness.

Genetic diversity of influenza A viruses circulating in Bulgaria during the 2018-2019 winter season

Introduction

Influenza viruses evolve rapidly and change their antigenic characteristics, necessitating biannual updates of flu vaccines.

Aim

The aim of this study was to characterize influenza viruses circulating in Bulgaria during the 2018/2019 season and to identify amino acid substitutions in them that might impact vaccine effectiveness.

Methodology

Typing/subtyping of influenza viruses were performed using real-time Reverse Transcription-PCR (RT-PCR) and results of phylogenetic and amino acid sequence analyses of influenza strains are presented.

Results

A(H1N1)pdm09 (66 %) predominated over A(H3N2) (34 %) viruses, with undetected circulation of B viruses in the 2018/2019 season. All A(H1N1)pdm09 viruses studied fell into the recently designated 6B.1A subclade with over 50 % falling in four subgroups: 6B.1A2, 6B.1A5, 6B.1A6 and 6B.1A7. Analysed A(H3N2) viruses belonged to subclades 3C.2a1b and 3C.2a2. Amino acid sequence analysis of 36 A(H1N1)pdm09 isolates revealed the presence of six–ten substitutions in haemagglutinin (HA), compared to the A/Michigan/45/2015 vaccine virus, three of which occurred in antigenic sites Sa and Cb, together with four–nine changes at positions in neuraminidase (NA), and a number of substitutions in internal proteins. HA1 D222N substitution, associated with increased virulence, was identified in two A(H1N1)pdm09 viruses. Despite the presence of several amino acid substitutions, A(H1N1)pdm09 viruses remained antigenically similar to the vaccine virus. The 28 A(H3N2) viruses characterized carried substitutions in HA, including some in antigenic sites A, B, C and E, in NA and internal protein sequences.

Conclusion

The results of this study showed the genetic diversity of circulating influenza viruses and the need for continuous antigenic and molecular surveillance.

Influenza hemagglutinin antigenic distance measures capture trends in HAI differences and infection outcomes, but are not suitable predictive tools

Vaccination is the most effective method to combat influenza. Vaccine effectiveness is influenced by the antigenic distance between the vaccine strain and the actual circulating virus. Amino acid sequence based methods of quantifying the antigenic distance were designed to predict influenza vaccine effectiveness in humans. The use of these antigenic distance measures has been proposed as an additive method for seasonal vaccine selection. In this report, several antigenic distance measures were evaluated as predictors of hemagglutination inhibition titer differences and clinical outcomes following influenza vaccination or infection in mice or ferrets. The antigenic distance measures described the increasing trend in the change of HAI titer, lung viral titer and percent weight loss in mice and ferrets. However, the variability of outcome variables produced wide prediction intervals for any given antigenic distance value. The amino acid substitution based antigenic distance measures were no better predictors of viral load and weight loss than HAI titer differences, the current predictive measure of immunological correlate of protection for clinical signs after challenge.

Epidemiological and evolutionary dynamics of influenza B virus in coastal Kenya as revealed by genomic analysis of strains sampled over a single season

The genomic epidemiology of influenza B virus (IBV) remains understudied in Africa despite significance to design of effective local and global control strategies. We undertook surveillance throughout 2016 in coastal Kenya, recruiting individuals presenting with acute respiratory illness at nine outpatient health facilities (any age) or admitted to the Kilifi County Hospital (<5 years old). Whole genomes were sequenced for a selected 111 positives; 94 (84.7%) of B/Victoria lineage and 17 (15.3%) of B/Yamagata lineage. Inter-lineage reassortment was detected in ten viruses; nine with B/Yamagata backbone but B/Victoria NA and NP segments and one with a B/Victoria backbone but B/Yamagata PB2, PB1, PA, and MP segments. Five phylogenomic clusters were identified among the sequenced viruses; (i), pure B/Victoria clade 1A (n = 93, 83.8%), (ii), reassortant B/Victoria clade 1A (n = 1, 0.9%), (iii), pure B/Yamagata clade 2 (n = 2, 1.8%), (iv), pure B/Yamagata clade 3 (n = 6, 5.4%), and (v), reassortant B/Yamagata clade 3 (n = 9, 8.1%). Using divergence dates and clustering patterns in the presence of global background sequences, we counted up to twenty-nine independent IBV strain introductions into the study area (∼900 km2) in 2016. Local viruses, including the reassortant B/Yamagata strains, clustered closely with viruses from neighbouring Tanzania and Uganda. Our study demonstrated that genomic analysis provides a clearer picture of locally circulating IBV diversity. The high number of IBV introductions highlights the challenge in controlling local influenza epidemics by targeted approaches, for example, sub-population vaccination or patient quarantine. The finding of divergent IBV strains co-circulating within a single season emphasises why broad immunity vaccines are the most ideal for influenza control in Kenya.

The Ecology and Evolution of Influenza Viruses

The patterns and processes of influenza virus evolution are of fundamental importance, underpinning such traits as the propensity to emerge in new host species and the ability to rapidly generate antigenic variation. Herein, we review key aspects of the ecology and evolution of influenza viruses. We begin with an exploration of the origins of influenza viruses within the orthomyxoviruses, showing how our perception of the evolutionary history of these viruses has been transformed with metagenomic sequencing. We then outline the diversity of virus subtypes in different species and the processes by which these viruses have emerged in new hosts, with a particular focus on the role played by segment reassortment. We then turn our attention to documenting the spread and phylodynamics of seasonal influenza A and B viruses in human populations, including the drivers of antigenic evolution, and finish with a discussion of virus diversity and evolution at the scale of individual hosts.

Within-Host Viral Diversity: A Window into Viral Evolution

The evolutionary dynamics of a virus can differ within hosts and across populations. Studies of within-host evolution provide an important link between experimental studies of virus evolution and large-scale phylodynamic analyses. They can determine the extent to which global processes are recapitulated on local scales and how accurately experimental infections model natural ones. They may also inform epidemiologic models of disease spread and reveal how host-level dynamics contribute to a virus's evolution at a larger scale. Over the last decade, advances in viral sequencing have enabled detailed studies of viral genetic diversity within hosts. I review how within-host diversity is sampled, measured, and expressed, and how comparative studies of viral diversity can be leveraged to elucidate a virus's evolutionary dynamics. These concepts are illustrated with detailed reviews of recent research on the within-host evolution of influenza virus, dengue virus, and cytomegalovirus.

Massive parallelization boosts big Bayesian multidimensional scaling

Big Bayes is the computationally intensive co-application of big data and large, expressive Bayesian models for the analysis of complex phenomena in scientific inference and statistical learning. Standing as an example, Bayesian multidimensional scaling (MDS) can help scientists learn viral trajectories through space-time, but its computational burden prevents its wider use. Crucial MDS model calculations scale quadratically in the number of observations. We partially mitigate this limitation through massive parallelization using multi-core central processing units, instruction-level vectorization and graphics processing units (GPUs). Fitting the MDS model using Hamiltonian Monte Carlo, GPUs can deliver more than 100-fold speedups over serial calculations and thus extend Bayesian MDS to a big data setting. To illustrate, we employ Bayesian MDS to infer the rate at which different seasonal influenza virus subtypes use worldwide air traffic to spread around the globe. We examine 5392 viral sequences and their associated 14 million pairwise distances arising from the number of commercial airline seats per year between viral sampling locations. To adjust for shared evolutionary history of the viruses, we implement a phylogenetic extension to the MDS model and learn that subtype H3N2 spreads most effectively, consistent with its epidemic success relative to other seasonal influenza subtypes. Finally, we provide MassiveMDS, an open-source, stand-alone C++ library and rudimentary R package, and discuss program design and high-level implementation with an emphasis on important aspects of computing architecture that become relevant at scale.

The impact of climate and antigenic evolution on seasonal influenza virus epidemics in Australia

Although seasonal influenza viruses circulate globally, prevention and treatment occur at the level of regions, cities, and communities. At these scales, the timing, duration and magnitude of epidemics vary substantially, but the underlying causes of this variation are poorly understood. Here, based on analyses of a 15-year city-level dataset of 18,250 laboratory-confirmed and antigenically-characterised influenza virus infections from Australia, we investigate the effects of previously hypothesised environmental and virological drivers of influenza epidemics. We find that anomalous fluctuations in temperature and humidity do not predict local epidemic onset timings. We also find that virus antigenic change has no consistent effect on epidemic size. In contrast, epidemic onset time and heterosubtypic competition have substantial effects on epidemic size and composition. Our findings suggest that the relationship between influenza population immunity and epidemiology is more complex than previously supposed and that the strong influence of short-term processes may hinder long-term epidemiological forecasts.

Dynamic interactions of influenza viruses in Hong Kong during 1998-2018

Influenza epidemics cause substantial morbidity and mortality every year worldwide. Currently, two influenza A subtypes, A(H1N1) and A(H3N2), and type B viruses co-circulate in humans and infection with one type/subtype could provide cross-protection against the others. However, it remains unclear how such ecologic competition via cross-immunity and antigenic mutations that allow immune escape impact influenza epidemic dynamics at the population level. Here we develop a comprehensive model-inference system and apply it to study the evolutionary and epidemiological dynamics of the three influenza types/subtypes in Hong Kong, a city of global public health significance for influenza epidemic and pandemic control. Utilizing long-term influenza surveillance data since 1998, we are able to estimate the strength of cross-immunity between each virus-pairs, the timing and frequency of punctuated changes in population immunity in response to antigenic mutations in influenza viruses, and key epidemiological parameters over the last 20 years including the 2009 pandemic. We find evidence of cross-immunity in all types/subtypes, with strongest cross-immunity from A(H1N1) against A(H3N2). Our results also suggest that A(H3N2) may undergo antigenic mutations in both summers and winters and thus monitoring the virus in both seasons may be important for vaccine development. Overall, our study reveals intricate epidemiological interactions and underscores the importance of simultaneous monitoring of population immunity, incidence rates, and viral genetic and antigenic changes.

Multi-task learning sparse group lasso: a method for quantifying antigenicity of influenza A(H1N1) virus using mutations and variations in glycosylation of Hemagglutinin

BACKGROUND

In addition to causing the pandemic influenza outbreaks of 1918 and 2009, subtype H1N1 influenza A viruses (IAVs) have caused seasonal epidemics since 1977. Antigenic property of influenza viruses are determined by both protein sequence and N-linked glycosylation of influenza glycoproteins, especially hemagglutinin (HA). The currently available computational methods are only considered features in protein sequence but not N-linked glycosylation.

RESULTS

A multi-task learning sparse group least absolute shrinkage and selection operator (LASSO) (MTL-SGL) regression method was developed and applied to derive two types of predominant features including protein sequence and N-linked glycosylation in hemagglutinin (HA) affecting variations in serologic data for human and swine H1N1 IAVs. Results suggested that mutations and changes in N-linked glycosylation sites are associated with the rise of antigenic variants of H1N1 IAVs. Furthermore, the implicated mutations are predominantly located at five reported antibody-binding sites, and within or close to the HA receptor binding site. All of the three N-linked glycosylation sites (i.e. sequons NCSV at HA 54, NHTV at HA 125, and NLSK at HA 160) identified by MTL-SGL to determine antigenic changes were experimentally validated in the H1N1 antigenic variants using mass spectrometry analyses. Compared with conventional sparse learning methods, MTL-SGL achieved a lower prediction error and higher accuracy, indicating that grouped features and MTL in the MTL-SGL method are not only able to handle serologic data generated from multiple reagents, supplies, and protocols, but also perform better in genetic sequence-based antigenic quantification.

CONCLUSIONS

In summary, the results of this study suggest that mutations and variations in N-glycosylation in HA caused antigenic variations in H1N1 IAVs and that the sequence-based antigenicity predictive model will be useful in understanding antigenic evolution of IAVs.

Structure-Guided Molecular Grafting of a Complex Broadly Neutralizing Viral Epitope

Antigenic variation and viral evolution have thwarted traditional influenza vaccination strategies. The broad protection afforded by a "universal" influenza vaccine may come from immunogens that elicit humoral immune responses targeting conserved epitopes on the viral hemagglutinin (HA), such as the receptor-binding site (RBS). Here, we engineered candidate immunogens that use noncirculating, avian influenza HAs as molecular scaffolds to present the broadly neutralizing RBS epitope from historical, circulating H1 influenzas. These "resurfaced" HAs (rsHAs) remove epitopes potentially targeted by strain-specific responses in immune-experienced individuals. Through structure-guided optimization, we improved two antigenically different scaffolds to bind a diverse panel of pan-H1 and H1/H3 cross-reactive bnAbs with high affinity. Subsequent serological and single germinal center B cell analyses from murine prime-boost immunizations show that the rsHAs are both immunogenic and can augment the quality of elicited RBS-directed antibodies. Our structure-guided, RBS grafting approach provides candidate immunogens for selectively presenting a conserved viral epitope.

An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver

We present a flexible, open source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.

Epidemiology and Genetic Variability of Circulating Influenza B Viruses in Uruguay, 2012-2019

Influenza B viruses (IBV) are an important cause of morbidity and mortality during interpandemic periods in the human population. Two phylogenetically distinct IBV lineages, B/Yamagata and B/Victoria, co-circulate worldwide and they present challenges for vaccine strain selection. Until the present study, there was little information regarding the pattern of the circulating strains of IBV in Uruguay. A subset of positive influenza B samples from influenza-like illness (ILI) outpatients and severe acute respiratory illness (SARI) inpatients detected in sentinel hospitals in Uruguay during 2012-2019 were selected. The sequencing of the hemagglutinin (HA) and neuraminidase (NA) genes showed substitutions at the amino acid level. Phylogenetic analysis reveals the co-circulation of both lineages in almost all seasonal epidemics in Uruguay, and allows recognizing a lineage-level vaccine mismatch in approximately one-third of the seasons studied. The epidemiological results show that the proportion of IBV found in ILI was significantly higher than the observed in SARI cases across different groups of age (9.7% ILI, 3.2% SARI) and patients between 5-14 years constituted the majority (33%) of all influenza B infection (p < 0.05). Interestingly, we found that individuals >25 years were particularly vulnerable to Yamagata lineage infections.

Structural insights into the potential changes in receptor binding site found in the 1998-2018 influenza B/Yamagata hemagglutinin: A putative correlation between receptor binding site structural variability and seasonal infection

Influenza B virus has two distinct lineages (Victoria and Yamagata) and are associated with seasonal influenza epidemics that cause respiratory illness. Influenza B hemagglutinin (HA) is a major surface glycoprotein with the receptor-binding site (RBS) primarily involved in viral pathogenesis. Generally, influenza B exclusively infects the human population which would insinuate that the structural variability of the influenza B HA RBS rarely changes. However, to our knowledge, the potential impact of variations in the influenza B HA RBS structural variability was not fully elucidated. Throughout this study, we generated models from the transitioning (evolving viral lineage) 1998-2018 influenza B/Yamagata HA, verified the quality of each HA model, performed HA RBS structural variability measurements, superimposed varying HA models for comparison, and designed a phylogenetic tree network for further analyses. We found that measurements of the transitioning HA RBS structural variability were generally maintained and, similarly, measurements of the altered (years that differed from the evolving viral lineage, specifically 2003, 2007, 2017) HA RBS structural variability differed from the transitioning HA RBS. Moreover, we observed that the altered HA RBS structural variability favored the formation of a putative Y202-H191 hydrogen bond which we postulate may increase structural stability, thereby, allowing for a winter infection of the virus. Furthermore, we established that changes in HA RBS structural variability does not influence viral evolution, but putatively seasonal infection.

Effects of Sequential Influenza A(H1N1)pdm09 Vaccination on Antibody Waning

BACKGROUND

Antibody waning following influenza vaccination has been repeatedly evaluated, but waning has rarely been studied in the context of longitudinal vaccination history.

METHODS

We developed a Bayesian hierarchical model to assess the effects of sequential influenza A(H1N1)pdm09 vaccination on hemagglutination inhibition antibody boosting and waning in a longitudinal cohort of older children and adults from 2011 to 2016, a period during which the A(H1N1)pdm09 vaccine strain did not change.

RESULTS

Antibody measurements from 2057 serum specimens longitudinally collected from 388 individuals were included. Average postvaccination antibody titers were similar across successive vaccinations, but the rate of antibody waning increased with each vaccination. The antibody half-life was estimated to decrease from 32 months (95% credible interval [CrI], 22-61 months) following first vaccination to 9 months (95% CrI, 7-15 months) following a seventh vaccination.

CONCLUSIONS

Although the rate of antibody waning increased with successive vaccination, the estimated antibody half-life was longer than a typical influenza season even among the most highly vaccinated. This supports current recommendations for vaccination at the earliest opportunity. Patterns of boosting and waning might be different with the influenza A(H3N2) subtype, which evolves more rapidly and has been most associated with reduced effectiveness following repeat vaccination.

Evolutionary dynamics of influenza B strains detected from paediatric acute respiratory infections in central Vietnam

Influenza virus B belongs to the family Orthomyxoviridae with segmented negative-sense RNA genomes. Since 1970s, influenza B has diverged intoVictoria and Yamagata, which differs in antigenic and evolutionary characteristics. Yet, molecular-epidemiological information of influenza B from developing nations is limited. In central Vietnam, influenza A subtype-specific circulation pattern and clinical characteristics were previously described. However, molecular evolutionary characteristics of influenza B has not been discussed to date. We utilized the influenza B positives obtained from paediatric ARI surveillance during 2007-2013. Influenza B HA and NA genes were amplified, sequenced, and phylogenetic/molecular evolutionary analysis was performed using Maximum Likelihood and Bayesian MCMC. Phylodynamics analysis was performed with Bayesian Skyline Plot (BSP). Furthermore, we performed selection pressure analysis and estimated N-glycosylation sites. In the current study, overall positive rate for influenza B was 3.0%, and Victoria lineage immediately became predominant in post-A/H1N1pdm09 period. The noticeable shift in Victoria lineage WHO Group occurred. With respect to the evolutionary rate (substitutions/site/year), Victoria lineage HA gene was evolving faster than Yamagata lineage (2.43 × 10^-3 vs 2.00 × 10^-3). Furthermore, the evolutionary rate of Victoria Group 5 was greater than Group 1. BSP presented the rapid growth in Effective Population Size (EPS) of Victoria lineage occurred soon after the 1st A/H1N1pdm09 case was detected whereas the EPS of Yamagata lineage was stable for both genes. N-glycosylation pattern between lineages and among WHO Groups were slightly different, and HA gene had a total of 6 amino acid substitutions under positive section pressure (4 for Victoria and 2 for Yamagata). The current results highlight the importance of Victoria lineage in post-A/H1N1pdm09 period. Difference in evolutionary characteristics and phylodynamics may indicate lineage and WHO Group-specific evolutionary dynamics. It is necessary to further continue the molecular-epidemiological surveillance in local setting to gain a better understanding of local evolutionary characteristics of influenza B strains.

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.

Influenza Vaccine Effectiveness among Elementary School Students in Korea during the 2016-2017 Seasons: a Cross-Sectional Survey

BACKGROUND

Annual vaccination is the principal way to reduce the mortality and morbidity associated with influenza. In the 2016-2017 influenza seasons, the influenza epidemic appeared to exhibit a different pattern from the previous years. Because of the unusual trend, the incidence of influenza-like patients among school-aged children had increased, causing doubts about the effectiveness of the influenza vaccine. Therefore, this study aimed to evaluate the effectiveness of the influenza vaccine among elementary school students in Korea.

METHODS

The study was conducted in elementary schools in each province of Korea in cooperation with the Student Health Policy Division of the Ministry of Education. Each Provincial Office of Education of Korea, except for Jeju, randomly selected one to two elementary schools for each District Office of Education. A total of 2,739 elementary school students were enrolled and vaccination and influenza infection status were collected from the subjects' parents through questionnaires, from February 13th to 21st in 2017. Vaccine effectiveness was defined as calculating the infection rate of influenza among the vaccinated and unvaccinated groups and determining the decreased infection rate of the vaccinated group relative to the unvaccinated group, while adjusting for time of vaccination and infection.

RESULTS

Adjusting for the interval between vaccination and infection, vaccine effectiveness of influenza was 17.6% (95% confidence interval [CI], 4.6% to 28.9%), 22.5% (95% CI, 10.3% to 33%), and 28.7% (95% CI, 17.5% to 38.3%) at 2 or more weeks, 3 or more weeks, and 4 or more weeks after vaccination, respectively.

CONCLUSION

In conclusion, considering the time required for adequate immunogenicity, the 2016-2017 seasonal influenza vaccine effectiveness in Korean elementary school students was 17.6%-28.7%, which was less effective than that of previous years.

Genetic characterization of influenza A(H3N2) viruses circulating in coastal Kenya, 2009-2017

Background

Influenza viruses evolve rapidly and undergo immune driven selection, especially in the hemagglutinin (HA) protein. We report amino acid changes affecting antigenic epitopes and receptor‐binding sites of A(H3N2) viruses circulating in Kilifi, Kenya, from 2009 to 2017.

Methods

Next‐generation sequencing (NGS) was used to generate A(H3N2) virus genomic data from influenza‐positive specimens collected from hospital admissions and health facility outpatients presenting with acute respiratory illness to health facilities within the Kilifi Health and Demographic Surveillance System. Full‐length HA sequences were utilized to characterize A(H3N2) virus genetic and antigenic changes.

Results

From 186 (90 inpatient and 96 outpatient) influenza A virus‐positive specimens processed, 101 A(H3N2) virus whole genomes were obtained. Among viruses identified in inpatient specimens from 2009 to 2015, divergence of circulating A(H3N2) viruses from the vaccine strains A/Perth/16/2009, A/Texas/50/2012, and A/Switzerland/9715293/2013 formed 6 genetic clades (A/Victoria/208/2009‐like, 3B, 3C, 3C.2a, 4, and 7). Among viruses identified in outpatient specimens from 2015 to 2017, divergence of circulating A(H3N2) viruses from vaccine strain A/Hong Kong/4801/2014 formed clade 3C.2a, subclades 3C.2a2 and 3C.2a3, and subgroup 3C.2a1b. Several amino acid substitutions were associated with the continued genetic evolution of A(H3N2) strains in circulation.

Conclusions

Our results suggest continuing evolution of currently circulating A(H3N2) viruses in Kilifi, coastal Kenya and suggest the need for continuous genetic and antigenic viral surveillance of circulating seasonal influenza viruses with broad geographic representation to facilitate prompt and efficient selection of influenza strains for inclusion in future influenza vaccines.

Preexisting subtype immunodominance shapes memory B cell recall response to influenza vaccination

Influenza is a highly contagious viral pathogen with more than 200,000 cases reported in the United States during the 2017-2018 season. Annual vaccination is recommended by the World Health Organization, with the goal to reduce influenza severity and transmission. Currently available vaccines are about 60% effective, and vaccine effectiveness varies from season to season, as well as between different influenza subtypes within a single season. Immunological imprinting from early-life influenza infection can prominently shape the immune response to subsequent infections. Here, the impact of preexisting B cell memory in the response to quadrivalent influenza vaccine was assessed using blood samples collected from healthy subjects (18-85 years old) prior to and 21-28 days following influenza vaccination. Influenza vaccination increased both HA-specific antibodies and memory B cell frequency. Despite no apparent differences in antigenicity between vaccine components, most individuals were biased toward one of the vaccine strains. Specifically, responses to H3N2 were reduced in magnitude relative to the other vaccine components. Overall, this study unveils a potentially new mechanism underlying differential vaccine effectiveness against distinct influenza subtypes.

Human infection with a novel reassortant Eurasian-avian lineage swine H1N1 virus in northern China

Influenza A virus infections occur in different species, causing mild to severe respiratory symptoms that lead to a heavy disease burden. Eurasian avian-like swine influenza A(H1N1) viruses (EAS-H1N1) are predominant in pigs and occasionally infect humans. An influenza A(H1N1) virus was isolated from a boy who was suffering from fever and headache and designated as A/Tianjin-baodi/1606/2018(H1N1). Full-genome sequencing and phylogenetic analysis revealed that A/Tianjin-baodi/1606/2018(H1N1) is a novel reassortant EAS-H1N1 containing gene segments from EAS-H1N1 (HA and NA), classical swine H1N1(NS) and A(H1N1)pdm09(PB2, PB2, PA, NP and M) viruses. The isolation and analysis of A/Tianjin-baodi/1606/2018(H1) provide further evidence that EAS-H1N1 poses a threat to human health and greater attention should be paid to surveillance of influenza virus infection in pigs and humans.

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.

Childhood immune imprinting to influenza A shapes birth year-specific risk during seasonal H1N1 and H3N2 epidemics

Across decades of co-circulation in humans, influenza A subtypes H1N1 and H3N2 have caused seasonal epidemics characterized by different age distributions of cases and mortality. H3N2 causes the majority of severe, clinically attended cases in high-risk elderly cohorts, and the majority of overall deaths, whereas H1N1 causes fewer deaths overall, and cases shifted towards young and middle-aged adults. These contrasting age profiles may result from differences in childhood imprinting to H1N1 and H3N2 or from differences in evolutionary rate between subtypes. Here we analyze a large epidemiological surveillance dataset to test whether childhood immune imprinting shapes seasonal influenza epidemiology, and if so, whether it acts primarily via homosubtypic immune memory or via broader, heterosubtypic memory. We also test the impact of evolutionary differences between influenza subtypes on age distributions of cases. Likelihood-based model comparison shows that narrow, within-subtype imprinting shapes seasonal influenza risk alongside age-specific risk factors. The data do not support a strong effect of evolutionary rate, or of broadly protective imprinting that acts across subtypes. Our findings emphasize that childhood exposures can imprint a lifelong immunological bias toward particular influenza subtypes, and that these cohort-specific biases shape epidemic age distributions. As a consequence, newer and less "senior" antibody responses acquired later in life do not provide the same strength of protection as responses imprinted in childhood. Finally, we project that the relatively low mortality burden of H1N1 may increase in the coming decades, as cohorts that lack H1N1-specific imprinting eventually reach old age.

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.

Phylodynamic theory of persistence, extinction and speciation of rapidly adapting pathogens

Rapidly evolving pathogens like influenza viruses can persist by changing their antigenic properties fast enough to evade the adaptive immunity, yet they rarely split into diverging lineages. By mapping the multi-strain Susceptible-Infected-Recovered model onto the traveling wave model of adapting populations, we demonstrate that persistence of a rapidly evolving, Red-Queen-like state of the pathogen population requires long-ranged cross-immunity and sufficiently large population sizes. This state is unstable and the population goes extinct or 'speciates' into two pathogen strains with antigenic divergence beyond the range of cross-inhibition. However, in a certain range of evolutionary parameters, a single cross-inhibiting population can exist for times long compared to the time to the most recent common ancestor ([Formula: see text]) and gives rise to phylogenetic patterns typical of influenza virus. We demonstrate that the rate of speciation is related to fluctuations of [Formula: see text] and construct a 'phase diagram' identifying different phylodynamic regimes as a function of evolutionary parameters.

Mapping person-to-person variation in viral mutations that escape polyclonal serum targeting influenza hemagglutinin

A longstanding question is how influenza virus evolves to escape human immunity, which is polyclonal and can target many distinct epitopes. Here, we map how all amino-acid mutations to influenza's major surface protein affect viral neutralization by polyclonal human sera. The serum of some individuals is so focused that it selects single mutations that reduce viral neutralization by over an order of magnitude. However, different viral mutations escape the sera of different individuals. This individual-to-individual variation in viral escape mutations is not present among ferrets that have been infected just once with a defined viral strain. Our results show how different single mutations help influenza virus escape the immunity of different members of the human population, a phenomenon that could shape viral evolution and disease susceptibility.

Recalling the Future: Immunological Memory Toward Unpredictable Influenza Viruses

Persistent and durable immunological memory forms the basis of any successful vaccination protocol. Generation of pre-existing memory B cell and T cell pools is thus the key for maintaining protective immunity to seasonal, pandemic and avian influenza viruses. Long-lived antibody secreting cells (ASCs) are responsible for maintaining antibody levels in peripheral blood. Generated with CD4⁺ T help after naïve B cell precursors encounter their cognate antigen, the linked processes of differentiation (including Ig class switching) and proliferation also give rise to memory B cells, which then can change rapidly to ASC status after subsequent influenza encounters. Given that influenza viruses evolve rapidly as a consequence of antibody-driven mutational change (antigenic drift), the current influenza vaccines need to be reformulated frequently and annual vaccination is recommended. Without that process of regular renewal, they provide little protection against “drifted” (particularly H3N2) variants and are mainly ineffective when a novel pandemic (2009 A/H1N1 “swine” flu) strain suddenly emerges. Such limitation of antibody-mediated protection might be circumvented, at least in part, by adding a novel vaccine component that promotes cross-reactive CD8⁺ T cells specific for conserved viral peptides, presented by widely distributed HLA types. Such “memory” cytotoxic T lymphocytes (CTLs) can rapidly be recalled to CTL effector status. Here, we review how B cells and follicular T cells are elicited following influenza vaccination and how they survive into a long-term memory. We describe how CD8⁺ CTL memory is established following influenza virus infection, and how a robust CTL recall response can lead to more rapid virus elimination by destroying virus-infected cells, and recovery. Exploiting long-term, cross-reactive CTL against the continuously evolving and unpredictable influenza viruses provides a possible mechanism for preventing a disastrous pandemic comparable to the 1918-1919 H1N1 “Spanish flu,” which killed more than 50 million people worldwide.

Epidemiological and genetic characterization of pH1N1 and H3N2 influenza viruses circulated in MENA region during 2009-2017

BACKGROUND

Influenza surveillance is necessary for detection of emerging variants of epidemiologic and clinical significance. This study describes the epidemiology of influenza types A and B, and molecular characteristics of surface glycoproteins (hemagglutinin [HA] and neuraminidase [NA]) of influenza A subtypes: pH1N1 and H3N2 circulated in Arabian Gulf, Levant and North Africa regions during 2009-2017.

METHODS

Analysis of phylogenetics and evolution of HA and NA genes was done using full HA and NA sequences (n = 1229) downloaded from Influenza Research Database (IRD).

RESULTS

In total, 130,354 influenza positive cases were reported to WHO during study period. Of these, 50.8% were pH1N1 positive, 15.9% were H3N2 positives and 17.2% were influenza B positive. With few exceptions, all three regions were showing the typical seasonal influenza peak similar to that reported in Northern hemisphere (December-March). However, influenza activity started earlier (October) in both Gulf and North Africa while commenced later during November in Levant countries. The molecular analysis of the HA genes (influenza A subtypes) revealed similar mutations to those reported worldwide. Generally, amino acid substitutions were most frequently found in head domain in H1N1 pandemic viruses, while localized mainly in the stem region in H3N2 viruses. Expectedly, seasons with high pH1N1 influenza activity was associated with a relatively higher number of substitutions in the head domain of the HA in pH1N1 subtype. Furthermore, nucleotide variations were lower at the antigenic sites of pH1N1 viruses compared to H3N2 viruses, which experienced higher variability at the antigenic sites, reflecting the increased immunological pressure because of longer circulation and continuous vaccine changes. Analysis of NA gene of pH1N1 viruses revealed sporadic detections of oseltamivir-resistance mutation, H275Y, in 4% of reported sequences, however, none of NAI resistance mutations were found in the NA of H3N2 viruses.

CONCLUSIONS

Molecular characterization of H1N1 and H3N2 viruses over 9 years revealed significant differences with regard to position and function of characterized substitutions. While pH1N1 virus substitutions were mainly found in HA head domain, H3N2 virus substitutions were mostly found in HA stem domain. Additionally, more fixed substitutions were encountered in H3N2 virus compared to larger number of non-fixed substitutions in pH1N1.

Genetic diversity of influenza A(H3N2) viruses in Northern Cameroon during the 2014-2016 influenza seasons

In Cameroon, genome characterization of influenza virus has been performed only in the Southern regions meanwhile genetic diversity of this virus varies with respect to locality. The Northern region characterized by a Sudan tropical climate might have distinct genetic characterization. This study aimed to better understand the genetic diversity of influenza A(H3N2) viruses circulating in Northern Cameroon. Sequences of three gene segments (hemagglutinin (HA), neuraminidase (NA) and matrix (M) genes) were obtained from 16 A(H3N2) virus strains collected during the 2014 to 2016 influenza seasons in Garoua. The HA gene segments were analysed with respect to reference strains while the NA and M gene was analysed for reported genetic markers of resistance to antivirals. Analysis of the HA sequences revealed that majority of the virus strains grouped together with the 2016-2017 vaccine strain (3C.2a-A/Hong Kong/4801/2014) while 3/5 (60%) of the 2015 viral strains grouped together with the 2015-2016 vaccine strain 3C.3a-A/Switzerland/9715293/2013. Within clade 3C.2a, Northern Cameroon sequences mostly grouped in sub-clade A3 (10/16). Analysis of the coding regions of the NA and M genes showed that none had genetic markers of resistance to neuraminidase inhibitors but all strains possessed the S31N substitution of resistance to amantadine. Due to some discrepancies observed in this region with respect to the Southern regions of Cameroon, there is necessity of including all regions within a country in the sentinel surveillance of influenza. These data will enable to track changes in influenza viruses in Cameroon.

Why Are CD8 T Cell Epitopes of Human Influenza A Virus Conserved?

Universal influenza vaccines against the conserved epitopes of influenza A virus have been proposed to minimize the burden of seasonal outbreaks and prepare for the pandemics. However, it is not clear how rapidly T cell-inducing vaccines will select for viruses that escape these T cell responses. Our mathematical models explore the factors that contribute to the conservation of CD8 T cell epitopes and how rapidly the virus will evolve in response to T cell-inducing vaccines. We identify the key biological parameters to be measured and questions that need to be addressed in future studies.

The high degree of conservation of CD8 T cell epitopes of influenza A virus (IAV) may allow for the development of T cell-inducing vaccines that provide protection across different strains and subtypes. This conservation is not fully explained by functional constraint, since an additional mutation(s) can compensate for the replicative fitness loss of IAV escape variants. Here, we propose three additional mechanisms that contribute to the conservation of CD8 T cell epitopes of IAV. First, influenza-specific CD8 T cells may protect predominantly against severe pathology rather than infection and may have only a modest effect on transmission. Second, polymorphism of the human major histocompatibility complex class I (MHC-I) gene restricts the advantage of an escape variant to only a small fraction of the human population who carry the relevant MHC-I alleles. Finally, infection with CD8 T cell escape variants may result in a compensatory increase in the responses to other epitopes of IAV. We use a combination of population genetics and epidemiological models to examine how the interplay between these mechanisms affects the rate of invasion of IAV escape variants. We conclude that for a wide range of biologically reasonable parameters, the invasion of an escape variant virus will be slow, with a timescale of a decade or more. The results suggest T cell-inducing vaccines do not engender the rapid evolution of IAV. Finally, we identify key parameters whose measurement will allow for more accurate quantification of the long-term effectiveness and impact of universal T cell-inducing influenza vaccines.

IMPORTANCE Universal influenza vaccines against the conserved epitopes of influenza A virus have been proposed to minimize the burden of seasonal outbreaks and prepare for the pandemics. However, it is not clear how rapidly T cell-inducing vaccines will select for viruses that escape these T cell responses. Our mathematical models explore the factors that contribute to the conservation of CD8 T cell epitopes and how rapidly the virus will evolve in response to T cell-inducing vaccines. We identify the key biological parameters to be measured and questions that need to be addressed in future studies.

Bursts of amino acid replacements in protein evolution

Evolution can occur both gradually and through alternating episodes of stasis and rapid changes. However, the prevalence and magnitude of fluctuations of the rate of evolution remain obscure. Detecting a rapid burst of changes requires a detailed record of past evolution, so that events that occurred within a short time interval can be identified. Here, we use the phylogenies of the Baikal Lake amphipods and of Catarrhini, which contain very short internal edges which make this task feasible. We detect six salient bursts of evolution of individual proteins during such short time periods, each involving between six and 38 amino acid substitutions. These bursts were extremely unlikely to have occurred neutrally, and were apparently caused by positive selection. On average, in the course of a time interval required for one synonymous substitution per site, a protein undergoes a strong burst of rapid evolution with probability at least approximately 0.01.

Mosaic nanoparticle display of diverse influenza virus hemagglutinins elicits broad B cell responses

The present vaccine against influenza virus has the inevitable risk of antigenic discordance between the vaccine and the circulating strains, which diminishes vaccine efficacy. This necessitates new approaches that provide broader protection against influenza. Here we designed a vaccine using the hypervariable receptor-binding domain (RBD) of viral hemagglutinin displayed on a nanoparticle (np) able to elicit antibody responses that neutralize H1N1 influenza viruses spanning over 90 years. Co-display of RBDs from multiple strains across time, so that the adjacent RBDs are heterotypic, provides an avidity advantage to cross-reactive B cells. Immunization with the mosaic RBD-np elicited broader antibody responses than those induced by an admixture of nanoparticles encompassing the same set of RBDs as separate homotypic arrays. Furthermore, we identified a broadly neutralizing monoclonal antibody in a mouse immunized with mosaic RBD-np. The mosaic antigen array signifies a unique approach that subverts monotypic immunodominance and allows otherwise subdominant cross-reactive B cell responses to emerge.

Current and future influenza vaccines

Although antiviral drugs and vaccines have reduced the economic and healthcare burdens of influenza, influenza epidemics continue to take a toll. Over the past decade, research on influenza viruses has revealed a potential path to improvement. The clues have come from accumulated discoveries from basic and clinical studies. Now, virus surveillance allows researchers to monitor influenza virus epidemic trends and to accumulate virus sequences in public databases, which leads to better selection of candidate viruses for vaccines and early detection of drug-resistant viruses. Here we provide an overview of current vaccine options and describe efforts directed toward the development of next-generation vaccines. Finally, we propose a plan for the development of an optimal influenza vaccine.

Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017

BACKGROUND

Although the burden of influenza is often discussed in the context of historical pandemics and the threat of future pandemics, every year a substantial burden of lower respiratory tract infections (LRTIs) and other respiratory conditions (like chronic obstructive pulmonary disease) are attributable to seasonal influenza. The Global Burden of Disease Study (GBD) 2017 is a systematic scientific effort to quantify the health loss associated with a comprehensive set of diseases and disabilities. In this Article, we focus on LRTIs that can be attributed to influenza.

METHODS

We modelled the LRTI incidence, hospitalisations, and mortality attributable to influenza for every country and selected subnational locations by age and year from 1990 to 2017 as part of GBD 2017. We used a counterfactual approach that first estimated the LRTI incidence, hospitalisations, and mortality and then attributed a fraction of those outcomes to influenza.

FINDINGS

Influenza LRTI was responsible for an estimated 145 000 (95% uncertainty interval [UI] 99 000-200 000) deaths among all ages in 2017. The influenza LRTI mortality rate was highest among adults older than 70 years (16·4 deaths per 100 000 [95% UI 11·6-21·9]), and the highest rate among all ages was in eastern Europe (5·2 per 100 000 population [95% UI 3·5-7·2]). We estimated that influenza LRTIs accounted for 9 459 000 (95% UI 3 709 000-22 935 000) hospitalisations due to LRTIs and 81 536 000 hospital days (24 330 000-259 851 000). We estimated that 11·5% (95% UI 10·0-12·9) of LRTI episodes were attributable to influenza, corresponding to 54 481 000 (38 465 000-73 864 000) episodes and 8 172 000 severe episodes (5 000 000-13 296 000).

INTERPRETATION

This comprehensive assessment of the burden of influenza LRTIs shows the substantial annual effect of influenza on global health. Although preparedness planning will be important for potential pandemics, health loss due to seasonal influenza LRTIs should not be overlooked, and vaccine use should be considered. Efforts to improve influenza prevention measures are needed.

FUNDING

Bill & Melinda Gates Foundation.

Predominance of influenza B/Yamagata lineage viruses in Bulgaria during the 2017/2018 season

In this study, we investigated the antigenic and genetic characteristics of influenza viruses circulating in Bulgaria during the 2017/2018 season. The detection and typing/subtyping of influenza viruses were performed using real-time RT-PCR. Results of antigenic characterisation, phylogenetic and amino acid sequence analyses of representative influenza strains are presented. The season was characterised by the predominance of B/Yamagata viruses, accounting for 77% of detected influenza viruses, followed by A(H1N1)pdm09 (17%), B/Victoria (3.7%) and A(H3N2) (2.4%). The sequenced B/Yamagata, B/Victoria, A(H1N1)pdm09 and A(H3N2) viruses belonged to the genetic groups 3, 1A, 6B.1 and 3C.2a1, respectively. Amino acid analysis of B/Yamagata isolates revealed the presence of three changes in haemagglutinin (HA), eight changes in neuraminidase (NA) and a number of substitutions in internal proteins compared with the B/Phucket/3073/2013 vaccine virus. Despite the amino acid changes, B/Yamagata viruses remained antigenically related to the vaccine strain. B/Victoria isolates fell into a group of viruses with double deletion (Δ162-163) in HA1. Substitutions in HA and NA sequences of B/Victoria, A(H1N1)pdm09 and A(H3N2) viruses were also identified compared with the vaccine strains, including in antigenic sites. The results of this study confirm the genetic variability of circulating influenza viruses and the need for continual antigenic and molecular surveillance.

Antigenic relationship among zoonotic flaviviruses from Italy

Here we report studies of the antigenic relationship of West Nile virus (WNV) and Usutu virus (USUV), two zoonotic flaviviruses from Italy, together with a Japanese encephalitis virus (JEV) strain and compared them with their genetic relationship using the immunodominant viral E protein. Thirty-nine isolates and reference strains were inactivated and used to immunize rabbits to produce hyper immune sera. Serum samples were tested by neutralization against all isolates and results visualized by generating antigenic map. Strains of WNV, USUV, and JEV grouped in separate clusters on the antigenic map. JEV was closer antigenically to USUV (mean of 3.5 Antigenic Unit, AU, equivalent to a 2-fold change in antibody titer) than to WNV strains (mean of 6 AU). A linear regression model predicted, on average, one unit of antigenic change, equivalent to a 2-fold change in antibody titer, for every 22 amino acid substitutions in the E protein ectodomain. Overall, antigenic map was demonstrated to be robust and consistent with phylogeny of the E protein. Indeed, the map provided a reliable means of visualizing and quantifying the relationship between these flaviviruses. Further antigenic analyses employing representative strains of extant serocomplexes are currently underway. This will provide a more in deep knowledge of antigenic relationships between flaviviruses.

Evolutionary Virology at 40

RNA viruses are diverse, abundant, and rapidly evolving. Genetic data have been generated from virus populations since the late 1970s and used to understand their evolution, emergence, and spread, culminating in the generation and analysis of many thousands of viral genome sequences. Despite this wealth of data, evolutionary genetics has played a surprisingly small role in our understanding of virus evolution. Instead, studies of RNA virus evolution have been dominated by two very different perspectives, the experimental and the comparative, that have largely been conducted independently and sometimes antagonistically. Here, we review the insights that these two approaches have provided over the last 40 years. We show that experimental approaches using in vitro and in vivo laboratory models are largely focused on short-term intrahost evolutionary mechanisms, and may not always be relevant to natural systems. In contrast, the comparative approach relies on the phylogenetic analysis of natural virus populations, usually considering data collected over multiple cycles of virus-host transmission, but is divorced from the causative evolutionary processes. To truly understand RNA virus evolution it is necessary to meld experimental and comparative approaches within a single evolutionary genetic framework, and to link viral evolution at the intrahost scale with that which occurs over both epidemiological and geological timescales. We suggest that the impetus for this new synthesis may come from methodological advances in next-generation sequencing and metagenomics.

Broadened immunity against influenza by vaccination with computationally designed influenza virus N1 neuraminidase constructs

Split inactivated influenza vaccines remain one of the primary preventative strategies against severe influenza disease in the population. However, current vaccines are only effective against a limited number of matched strains. The need for broadly protective vaccines is acute due to the high mutational rate of influenza viruses and multiple strain variants in circulation at any one time. The neuraminidase (NA) glycoprotein expressed on the influenza virion surface has recently regained recognition as a valuable vaccine candidate. We sought to broaden the protection provided by NA within the N1 subtype by computationally engineering consensus NA sequences. Three NA antigens (NA5200, NA7900, NA9100) were designed based on sequence clusters encompassing three major groupings of NA sequence space; (i) H1N1 2009 pandemic and Swine H1N1, (ii) historical seasonal H1N1 and (iii) H1N1 viruses ranging from 1933 till current times. Recombinant NA proteins were produced as a vaccine and used in a mouse challenge model. The design of the protein dictated the protection provided against the challenge strains. NA5200 protected against H1N1 pdm09, a Swine isolate from 1998 and NIBRG-14 (H5N1). NA7900 protected against all seasonal H1N1 viruses tested, and NA9100 showed the broadest range of protection covering all N1 viruses tested. By passive transfer studies and serological assays, the protection provided by the cluster-based consensus (CBC) designs correlated to antibodies capable of mediating NA inhibition. Importantly, sera raised to the consensus NAs displayed a broader pattern of reactivity and protection than naturally occurring NAs, potentially supporting a predictive approach to antigen design.

The high variability of the influenza virus — arising from its high mutation rate and wide range of strains — limits the effectiveness of influenza vaccines unless they induce a broad immune response, a difficult task when relying on natural viral antigens. Here, Xavier Saelens, Thorsten Vogel, Ray Oomen and colleagues applied a ‘cluster-based’ consensus computational approach to design three consensus sequences of the viral protein neuroaminidase (NA) subtype 1 that induce broadly protective immune responses in vaccinated mice. NA9100, a consensus NA sequence based on H1N1 virus strains collected from 1933 to today, was protective against all N1 viruses tested. By using a computational method to integrate multiple sequences of viral proteins into one consensus protein, the researchers provide a strategy that can be applied to develop broadly protective vaccine formulations for influenza virus.

Modeling site-specific amino-acid preferences deepens phylogenetic estimates of viral sequence divergence

Molecular phylogenetics is often used to estimate the time since the divergence of modern gene sequences. For highly diverged sequences, such phylogenetic techniques sometimes estimate surprisingly recent divergence times. In the case of viruses, independent evidence indicates that the estimates of deep divergence times from molecular phylogenetics are sometimes too recent. This discrepancy is caused in part by inadequate models of purifying selection leading to branch-length underestimation. Here we examine the effect on branch-length estimation of using models that incorporate experimental measurements of purifying selection. We find that models informed by experimentally measured site-specific amino-acid preferences estimate longer deep branches on phylogenies of influenza virus hemagglutinin. This lengthening of branches is due to more realistic stationary states of the models, and is mostly independent of the branch-length extension from modeling site-to-site variation in amino-acid substitution rate. The branch-length extension from experimentally informed site-specific models is similar to that achieved by other approaches that allow the stationary state to vary across sites. However, the improvements from all of these site-specific but time homogeneous and site independent models are limited by the fact that a protein’s amino-acid preferences gradually shift as it evolves. Overall, our work underscores the importance of modeling site-specific amino-acid preferences when estimating deep divergence times—but also shows the inherent limitations of approaches that fail to account for how these preferences shift over time.

Real-Time Analysis and Visualization of Pathogen Sequence Data

The rapid development of sequencing technologies has to led to an explosion of pathogen sequence data, which are increasingly collected as part of routine surveillance or clinical diagnostics. In public health, sequence data are used to reconstruct the evolution of pathogens, to anticipate future spread, and to target interventions. In clinical settings, whole-genome sequencing can identify pathogens at the strain level, can be used to predict phenotypes such as drug resistance and virulence, and can inform treatment by linking closely related cases. While sequencing has become cheaper, the analysis of sequence data has become an important bottleneck. Deriving interpretable and actionable results for a large variety of pathogens, each with its own complexity, from continuously updated data is a daunting task that requires flexible bioinformatic workflows and dissemination platforms. Here, we review recent developments in real-time analyses of pathogen sequence data, with a particular focus on the visualization and integration of sequence and phenotype data.

Evaluation of the field-protective effectiveness of seasonal influenza vaccine among Korean children aged < 5 years during the 2014-2015 and 2015-2016 influenza seasons: a cohort study

BACKGROUND

A field effectiveness evaluation of the influenza vaccine among children younger than five years is important due to the high burden of influenza in this age group. The epidemiology of influenza virus changes rapidly each year. Moreover, the development of a new type of influenza vaccine is accelerating, necessitating a new field effectiveness evaluation.

METHODS

This multi-center, open-label cohort study was conducted in the northern part of Seoul from December 2014 to May 2015 and in Gyeong-gi Province from December 2015 to May 2016. The cohort comprised an influenza vaccinated group and non-vaccinated group. During the influenza seasons, we conducted influenza rapid tests and polymerase chain reaction assays for individuals with suspected influenza and checked for the presence of influenza virus. We calculated the influenza vaccine effectiveness by comparing the incidence rates of influenza between the vaccinated and non-vaccinated groups.

RESULTS

During the 2014-2015 season, the field effectiveness of the influenza vaccine was 38.4%. In particular, the vaccine effectiveness against type A influenza virus was 50.7%. During the 2015-2016 season, the vaccine effectiveness reached 23.8% and the vaccine effectiveness against type A influenza virus was 48.5%. The vaccine effectiveness against influenza B virus was markedly reduced in both seasons.

CONCLUSION

The influenza vaccine was supposed to be effective against influenza A, but may have a limited effectiveness against influenza B among Korean children aged < 5 years.

Recent advances in computational phylodynamics

Time-stamped, trait-annotated phylogenetic trees built from virus genome data are increasingly used for outbreak investigation and monitoring ongoing epidemics. This routinely involves reconstructing the spatial and demographic processes from large data sets to help unveil the patterns and drivers of virus spread. Such phylodynamic inferences can however become quite time-consuming as the dimensions of the data increase, which has led to a myriad of approaches that aim to tackle this complexity. To elucidate the current state of the art in the field of phylodynamics, we discuss recent developments in Bayesian inference and accompanying software, highlight methods for improving computational efficiency and relevant visualisation tools. As an alternative to fully Bayesian approaches, we touch upon conditional software pipelines that compromise between statistical coherence and turn-around-time, and we highlight the available software packages. Finally, we outline future directions that may facilitate the large-scale tracking of epidemics in near real time.

Antigenic evolution of viruses in host populations

To escape immune recognition in previously infected hosts, viruses evolve genetically in immunologically important regions. The host’s immune system responds by generating new memory cells recognizing the mutated viral strains. Despite recent advances in data collection and analysis, it remains conceptually unclear how epidemiology, immune response, and evolutionary factors interact to produce the observed speed of evolution and the incidence of infection. Here we establish a general and simple relationship between long-term cross-immunity, genetic diversity, speed of evolution, and incidence. We develop an analytic method fusing the standard epidemiological susceptible-infected-recovered approach and the modern virus evolution theory. The model includes the factors of strain selection due to immune memory cells, random genetic drift, and clonal interference effects. We predict that the distribution of recovered individuals in memory serotypes creates a moving fitness landscape for the circulating strains which drives antigenic escape. The fitness slope (effective selection coefficient) is proportional to the reproductive number in the absence of immunity R₀ and inversely proportional to the cross-immunity distance a, defined as the genetic distance of a virus strain from a previously infecting strain conferring 50% decrease in infection probability. Analysis predicts that the evolution rate increases linearly with the fitness slope and logarithmically with the genomic mutation rate and the host population size. Fitting our analytic model to data obtained for influenza A H3N2 and H1N1, we predict the annual infection incidence within a previously estimated range, (4-7)%, and the antigenic mutation rate of U_b = (5 − 8) ⋅ 10⁻⁴ per transmission event per genome. Our prediction of the cross-immunity distance of a = (14 − 15) aminoacid substitutions agrees with independent data for equine influenza.

Spread of many RNA viruses in a population represents a competition between host immune responses and viral evolution. RNA viruses accumulate mutations in immunologically important regions to escape immune recognition in hosts previously exposed to infection, while the immune system responds by producing new memory cells. Despite recent advances in data collection and their analysis, it remains conceptually unclear how epidemiology, immune response, and evolutionary factors interact to produce the observed speed of evolution and its incidence. By combining the standard epidemiological approach with the modern theory of viral evolution, we predict a general relationship between long-term cross-immunity, antigenic diversity of virus, its evolution speed, infection incidence, and the time to the most recent common ancestor. We apply these theoretical findings to available data on influenza virus to determine two important parameters of its evolution and confirm the model. Current strategies of vaccination against influenza should take into account stochastic fluctuations in fitness effect of mutations predicted by the theory.

Influenza Virus-Host Co-evolution. A Predator-Prey Relationship?

Influenza virus continues to cause yearly seasonal epidemics worldwide and periodically pandemics. Although influenza virus infection and its epidemiology have been extensively studied, a new pandemic is likely. One of the reasons influenza virus causes epidemics is its ability to constantly antigenically transform through genetic diversification. However, host immune defense mechanisms also have the potential to evolve during short or longer periods of evolutionary time. In this mini-review, we describe the evolutionary procedures related with influenza viruses and their hosts, under the prism of a predator-prey relationship.

The Influenza B Virus Hemagglutinin Head Domain Is Less Tolerant to Transposon Mutagenesis than That of the Influenza A Virus

Influenza A and B viruses can continuously evade humoral immune responses by developing mutations in the globular head of the hemagglutinin (HA) that prevent antibody binding. However, the influenza B virus HA over time displays less antigenic variation despite being functionally and structurally similar to the influenza A virus HA. To determine if the influenza B virus HA is under constraints that limit its antigenic variation, we performed a transposon screen to compare the mutational tolerance of the currently circulating influenza A virus HAs (H1 and H3 subtypes) and influenza B virus HAs (B/Victoria87 and B/Yamagata88 antigenic lineages). A library of insertional mutants for each HA was generated and deep sequenced after passaging to determine where insertions were tolerated in replicating viruses. The head domains of both viruses tolerated transposon mutagenesis, but the influenza A virus head was more tolerant to insertions than the influenza B virus head domain. Furthermore, all five of the known antigenic sites of the influenza A virus HA were tolerant of 15 nucleotide insertions, while insertions were detected in only two of the four antigenic sites in the influenza B virus head domain. Our analysis demonstrated that the influenza B virus HA is inherently less tolerant of transposon-mediated insertions than the influenza A virus HA. The reduced insertional tolerance of the influenza B virus HA may reveal genetic restrictions resulting in a lower capacity for antigenic evolution.IMPORTANCE Influenza viruses cause seasonal epidemics and result in significant human morbidity and mortality. Influenza viruses persist in the human population through generating mutations in the hemagglutinin head domain that prevent antibody recognition. Despite the similar selective pressures on influenza A and B viruses, influenza A virus displays a higher rate and breadth of antigenic variability than influenza B virus. A transposon mutagenesis screen was used to examine if the reduced antigenic variability of influenza B virus was due to inherent differences in mutational tolerance. This study demonstrates that the influenza A virus head domain and the individual antigenic sites targeted by humoral responses are more tolerant to insertions than those of influenza B virus. This finding sheds light on the genetic factors controlling the antigenic evolution of influenza viruses.