On the use of hemagglutination-inhibition for influenza surveillance: surveillance data are predictive of influenza vaccine effectiveness

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

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

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

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

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

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

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

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

Antigenic Relatedness between Mannans from Coccidioides immitis and Coccidioides posadasii Spherules and Mycelia

Immunoassays for cell wall mannans that are excreted into serum and urine have been used as an aid in the diagnosis of many disseminated fungal infections, including coccidioidomycosis. Antigen-detection immunoassays are critically dependent on the detection of an analyte, such as mannan, by antibodies that are specific to the analyte. The goal of this study was to evaluate the extent of cross-reactivity of polyclonal antibodies raised against Coccidioides spp. Analysis of antigenic relatedness between mannans from C. posadasii and C. immitis spherules and mycelia showed complete relatedness when evaluated by the method of Archetti and Horsfall, which was originally used to study the antigenic relationships between Influenzae virus isolates. In a further effort to validate the suitability of the antigenic relatedness calculation methodology for polysaccharide antigens, we also applied the method of Archetti and Horsfall to published results that had previously identified the major capsular serotypes of Cryptococcus species. The results of this analysis showed that Archetti and Horsfall's antigenic relatedness calculation correctly identified the major cryptococcal serotypes. Together, these results suggest that the method is applicable to polysaccharide antigens, and that immunoassays that detect Coccidioides mannans are likely to have good reactivity across Coccidioides species (inclusivity) due to the species' high level of antigenic relatedness.

PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network

Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antigenic variation. Through 5-fold cross-validation and retrospective testing, PREDAC-CNN has shown superior performance in predicting antigenic variants compared to its counterparts. Additionally, PREDAC-CNN has been instrumental in identifying predominant antigenic clusters for A/H3N2 (1968-2023) and A/H1N1 (1977-2023) viruses, significantly aiding in vaccine strain recommendation.

CLCAP: Contrastive learning improves antigenicity prediction for influenza A virus using convolutional neural networks

Influenza viruses are detected year-round over the world and the viruses will usually circulate during fall and winter, causing the seasonal flu. The growing novel variants of influenza viruses pose a significant concern to public health annually. However, the rapid mutation of the influenza viruses makes it challenging to timely track their evolution. Therefore, a fast, low-cost, and precise method to predict the antigenic variant of influenza viruses could help vaccine development and prevent viral transmission. In this study, we propose a multi-channel convolutional neural network using contrastive learning to predict the antigenicity of influenza A viruses. An integrated dataset containing antigenic data and protein sequences was collected from various public resources and literature. The experimental results on three different influenza subtypes indicate our proposed model outperforms other traditional machine learning classifiers for antigenicity prediction. In addition, it also demonstrates superior performance over several state-of-the-art approaches, with 5.18 %, 7.03 % and 7.82 % increase in accuracy compared to the best results for H1N1, H3N2 and H5N1, respectively. The proposed framework is timely and effective in influenza antigenicity prediction and can be adapted to the study of other viruses.

IAV-CNN: A 2D Convolutional Neural Network Model to Predict Antigenic Variants of Influenza A Virus

The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we apply a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combining the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.

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.

End-to-end antigenic variant generation for H1N1 influenza HA protein using sequence to sequence models

The growing risk of new variants of the influenza A virus is the most significant to public health. The risk imposed from new variants may have been lethal, as witnessed in the year 2009. Even though the improvement in predicting antigenicity of influenza viruses has rapidly progressed, few studies employed deep learning methodologies. The most recent literature mostly relied on classification techniques, while a model that generates the HA protein of the antigenic variant is not developed. However, the antigenic pair of influenza virus A can be determined in a laboratory setup, the process needs a tremendous amount of time and labor. Antigenic shift and drift which are caused by changes in surface protein favored the influenza A virus in evading immunity. The high frequency of the minor changes in the surface protein poses a challenge to identifying the antigenic variant of an emerging virus. These changes slow down vaccine selection and the manufacturing process. In this vein, the proposed model could help save the time and efforts exerted to identify the antigenic pair of the influenza virus. The proposed model utilized an end-to-end learning methodology relying on deep sequence-to-sequence architecture to generate the antigenic variant of a given influenza A virus using surface protein. Employing the BLEU score to evaluate the generated HA protein of the antigenic variant of influenza virus A against the actual variant, the proposed model achieved a mean accuracy of 97.57%.

A statistical analysis of antigenic similarity among influenza A (H3N2) viruses

An accurate assessment of antigenic similarity between influenza viruses is important for vaccine strain recommendations and influenza surveillance. Due to the mechanisms that result in frequent changes in the antigenicities of strains, it is desirable to obtain an antigenic similarity measure that accounts for specific changes in strains that are of epidemiological importance in influenza. Empirically grounded statistical models best achieve this. In this study, an interpretable machine-learning model was developed using distinguishing features of antigenic variants to analyze antigenic similarity. The features comprised of cluster information, amino acid sequences located in known antigenic and receptor-binding sites of influenza A (H3N2). In order to assess validity of parameters, accuracy and relevance of model to vaccine effectiveness, the model was applied to influenza A (H3N2) viruses due to their abundant genetic data and epidemiological relevance to influenza surveillance. An application of the model revealed that all model parameters were statistically significant to determining antigenic similarity between strains. Furthermore, upon evaluating the model for predicting antigenic similarity between strains, it achieved 95% area under Receiver Operating Characteristic curve (AUC), 94% accuracy, 76% precision, 97% specificity, 68% sensitivity and a diagnostic odds ratio (DOR) of 83.19. Above all, the model was found to be strongly related to influenza vaccine effectiveness to indicate the correlation between vaccine effectiveness and antigenic similarity between vaccine and circulating strains in an epidemic. The study predicts probabilities of antigenic similarity and estimates changes in strains that lead to antigenic variants. A successful application of the methods presented in this study would complement the global efforts in influenza surveillance.

VirPreNet: a weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all eight segments

MOTIVATION

Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics.

RESULTS

In this article, we propose a weighted ensemble convolutional neural network (CNN) for the virulence prediction of influenza A viruses named VirPreNet that uses all eight segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the eight-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble CNN is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNet's main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza virulence.

AVAILABILITY AND IMPLEMENTATION

Codes and data to generate the VirPreNet are publicly available at https://github.com/Rayin-saber/VirPreNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluation of the immune response of a H7N9 candidate vaccine virus derived from the fifth wave A/Guangdong/17SF003/2016

Highly pathogenic influenza H7N9 viruses that emerged in the fifth wave of H7N9 outbreak pose a risk to human health. The World Health Organization has updated the candidate vaccine viruses for H7N9 viruses recently. In this study, we evaluated the immune response to an updated H7N9 candidate vaccine virus, which derived from the highly pathogenic A/Guangdong/17SF003/2016 (GD/16) in mice and rhesus macaques. GD/16 vaccination elicited robust neutralizing, virus-specific immunoglobulin G antibodies and effective protection, but poor hemagglutination inhibition antibody titers. Furthermore, mouse and rhesus macaque serum raised against the previous H7N9 CVV A/Anhui/1/2013 (AH/13) were tested for its cross-reactivity to GD/16 virus. We found that although AH/13-immune serum has poor hemagglutination inhibition reactivity against GD/16 virus, AH/13 elicit efficient cross-neutralizing antibodies and in vivo protection against GD/16. Further studies showed that the hemagglutinin of GD/16 has strong receptor binding avidity, which might be associated with the decreased hemagglutination inhibition assay sensitivity. This study underscores the point that receptor binding avidity should be taken into account when performing quantitative interpretation of hemagglutination inhibition data. A combination of multiple serological assays is required for accurate vaccine evaluation and antigenic analysis of influenza viruses.

A context-free encoding scheme of protein sequences for predicting antigenicity of diverse influenza A viruses

BACKGROUND

The evolution of influenza A viruses leads to the antigenic changes. Serological diagnosis of the antigenicity is usually labor-intensive, time-consuming and not suitable for early-stage detection. Computational prediction of the antigenic relationship between emerging and old strains of influenza viruses using viral sequences can facilitate large-scale antigenic characterization, especially for those viruses requiring high biosafety facilities, such as H5 and H7 influenza A viruses. However, most computational models require carefully designed subtype-specific features, thereby being restricted to only one subtype.

METHODS

In this paper, we propose a Context-FreeEncoding Scheme (CFreeEnS) for pairs of protein sequences, which encodes a protein sequence dataset into a numeric matrix and then feeds the matrix into a downstream machine learning model. CFreeEnS is not only free from subtype-specific selected features but also able to improve the accuracy of predicting the antigenicity of influenza. Since CFreeEnS is subtype-free, it is applicable to predicting the antigenicity of diverse influenza subtypes, hopefully saving the biologists from conducting serological assays for highly pathogenic strains.

RESULTS

The accuracy of prediction on each subtype tested (A/H1N1, A/H3N2, A/H5N1, A/H9N2) is over 85%, and can be as high as 91.5%. This outperforms existing methods that use carefully designed subtype-specific features. Furthermore, we tested the CFreeEnS on the combined dataset of the four subtypes. The accuracy reaches 84.6%, much higher than the best performance 75.1% reported by other subtype-free models, i.e. regional band-based model and residue-based model, for predicting the antigenicity of influenza. Also, we investigate the performance of CFreeEnS when the model is trained and tested on different subtypes (i.e. transfer learning). The prediction accuracy using CFreeEnS is 84.3% when the model is trained on the A/H1N1 dataset and tested on the A/H5N1, better than the 75.2% using a regional band-based model.

CONCLUSIONS

The CFreeEnS not only improves the prediction of antigenicity on datasets with only one subtype but also outperforms existing methods when tested on a combined dataset with four subtypes of influenza viruses.

Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model

H1N1 is the earliest emerging subtype of influenza A viruses with available genomic sequences, has caused several pandemics and seasonal epidemics, resulting in millions of deaths and enormous economic losses. Timely determination of new antigenic variants is crucial for the vaccine selection and flu prevention. In this study, we chronologically divided the H1N1 strains into several periods in terms of the epidemics and pandemics. Computational models have been constructed to predict antigenic variants based on epidemic and pandemic periods. By sequence analysis, we demonstrated the diverse mutation patterns of HA1 protein on different periods and that an individual model built upon each period can not represent the variations of H1N1 virus. A stacking model was established for the prediction of antigenic variants, combining all the variation patterns across periods, which would help assess a new influenza strain’s antigenicity. Three different feature extraction methods, i.e. residue-based, regional band-based and epitope region-based, were applied on the stacking model to verify its feasibility and robustness. The results showed the capability of determining antigenic variants prediction with accuracy as high as 0.908 which performed better than any of the single models. The prediction performance using the stacking model indicates clear distinctions of mutation patterns and antigenicity between epidemic and pandemic strains. It would also facilitate rapid determination of antigenic variants and influenza surveillance.

Anticipating time-dependent antigenic variants of influenza A (H3N2) viruses

Frequent variations in influenza vaccines are necessary to match antigenic variants which appear in influenza epidemics. Antigenic variants of influenza viruses result from frequent mutations in amino acid residues located on their hemagglutinin (HA) proteins. Knowledge of specific changes in these amino acids helps to characterize distinct antigenic variants. In this paper, statistical models are developed and used to investigate changes in amino acids which accompany antigenic variants of epidemiological importance. Amino acid sequences of the HA proteins of influenza A (H3N2) strains isolated from 1968 to 2015 were obtained. The sequences were aligned using Clustal Omega and the number of differences in amino acid residues located on annotated positions of antigenic sites of the HA protein between pairs of strains were determined. These were linked in the statistical models and used to assess the relationship between any pair of influenza strains. The results revealed that both antigenic similarity between strains and the amino acid changes are affected by the time of isolation of the strains. Furthermore, the models predicted that rates of changes in amino acids located on the antigenic sites ranged between 5% and 6% per site per year. The findings of the study suggest that time-dependent antigenic variants of influenza A (H3N2) strains may occur as they evolve. The study has the potential to greatly improve influenza surveillance in as much as it supports vaccine designs.

Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance

Haemagglutination inhibition (HI) assays are typically used for comparing and characterizing influenza viruses. Data obtained from the assays (titres) are used quantitatively to determine antigenic differences between influenza strains. However, the use of these titres has been criticized as they sometimes fail to capture accurate antigenic differences between strains. Our previous analytical work revealed how antigenic and non-antigenic variables contribute to the titres. Building on this previous work, we have developed a Bayesian method for decoupling antigenic and non-antigenic contributions to the titres in this paper. We apply this method to a compendium of HI titres of influenza A (H3N2) viruses curated from 1968 to 2016. Remarkably, the results of this fit indicate that the non-antigenic variable, which is inversely correlated with viral avidity for the red blood cells used in HI assays, oscillates during the course of influenza virus evolution, with a period that corresponds roughly to the timescale on which antigenic variants replace each other. Together, the results suggest that the new Bayesian method is applicable to the analysis of long-term dynamics of both antigenic and non-antigenic properties of influenza virus.

Different cross protection scopes of two avian influenza H5N1 vaccines against infection of layer chickens with a heterologous highly pathogenic virus

Avian influenza (AI) virus strains vary in antigenicity, and antigenic differences between circulating field virus and vaccine virus will affect the effectiveness of vaccination of poultry. Antigenic relatedness can be assessed by measuring serological cross-reactivity using haemagglutination inhibition (HI) tests. Our study aims to determine the relation between antigenic relatedness expressed by the Archetti-Horsfall ratio, and reduction of virus transmission of highly pathogenic H5N1 AI strains among vaccinated layers. Two vaccines were examined, derived from H5N1 AI virus strains A/Ck/WJava/Sukabumi/006/2008 and A/Ck/CJava/Karanganyar/051/2009. Transmission experiments were carried out in four vaccine and two control groups, with six sets of 16 specified pathogen free (SPF) layer chickens. Birds were vaccinated at 4weeks of age with one strain and challenge-infected with the homologous or heterologous strain at 8weeks of age. No transmission or virus shedding occurred in groups challenged with the homologous strain. In the group vaccinated with the Karanganyar strain, high cross-HI responses were observed, and no transmission of the Sukabumi strain occurred. However, in the group vaccinated with the Sukabumi strain, cross-HI titres were low, virus shedding was not reduced, and multiple transmissions to contact birds were observed. This study showed large differences in cross-protection of two vaccines based on two different highly pathogenic H5N1 virus strains. This implies that extrapolation of in vitro data to clinical protection and reduction of virus transmission might not be straightforward.

A simple mechanistic explanation for original antigenic sin and its alleviation by adjuvants

A large number of published studies have shown that adaptive immunity to a particular antigen, including pathogen-derived, can be boosted by another, cross-reacting antigen while inducing suboptimal immunity to the latter. Although this phenomenon, called original antigenic sin (OAS), was first reported approximately 70 years ago (Francis et al. 1947 Am. J. Public Health 37, 1013-1016 (doi:10.2105/AJPH.37.8.1013)), its underlying biological mechanisms are still inadequately understood (Kim et al. Proc. Natl Acad. Sci. USA 109, 13 751-13 756 (doi:10.1073/pnas.0912458109)). Here, focusing on the humoral aspects of adaptive immunity, I propose a simple and testable mechanism: that OAS occurs when T regulatory cells induced by the first antigen decrease the dose of the second antigen that is loaded by dendritic cells and available to activate naive lymphocytes. I use both a parsimonious mathematical model and experimental data to confirm the deductive validity of this proposal. This model also explains the puzzling experimental observation that administering certain dendritic cell-activating adjuvants during antigen exposure alleviates OAS. Specifically, the model predicts that such adjuvants will attenuate T regulatory suppression of naive lymphocyte activation. Together, these results suggest additional strategies for redeeming adaptive immunity from the destructive consequences of antigenic 'sin'.

Influenza evolution and H3N2 vaccine effectiveness, with application to the 2014/2015 season

Influenza A is a serious disease that causes significant morbidity and mortality, and vaccines against the seasonal influenza disease are of variable effectiveness. In this article, we discuss the use of the pepitope method to predict the dominant influenza strain and the expected vaccine effectiveness in the coming flu season. We illustrate how the effectiveness of the 2014/2015 A/Texas/50/2012 [clade 3C.1] vaccine against the A/California/02/2014 [clade 3C.3a] strain that emerged in the population can be estimated via pepitope In addition, we show by a multidimensional scaling analysis of data collected through 2014, the emergence of a new A/New Mexico/11/2014-like cluster [clade 3C.2a] that is immunologically distinct from the A/California/02/2014-like strains.

Profiles of influenza A/H1N1 vaccine response using hemagglutination-inhibition titers

To identify distinct antibody profiles among adults 50-to-74 years old using influenza A/H1N1 HI titers up to 75 days after vaccination. Healthy subjects 50 to 74 years old received the 2010-2011 trivalent inactivated influenza vaccine. We measured venous samples from Days 0, 28, and 75 for HI and VNA and B-cell ELISPOTs. Of 106 subjects, HI titers demonstrated a ceiling effect for 11 or 10% for those with a pre-vaccination HI titer of 1:640 where no subject post-vaccination had an increase in titer. Of the remaining 95 subjects, only 37 or 35% overall had at least a 4-fold increase by Day 28. Of these 37, 3 waned at least 4-fold, and 13 others 2-fold. Thus 15% of the subjects showed waning antibody titers by Day 75. More than half failed to respond at all. The profiles populated by these subjects as defined by HI did not vary with age or gender. The VNA results mimicked the HI profiles, but the profiles for B-cell ELISPOT did not. HI titers at Days 0, 28, and 75 populate 4 biologically plausible profiles. Limitations include lack of consensus for operationally defining waning as well as for the apparent ceiling. Furthermore, though well accepted as a marker for vaccine response, assigning thresholds with HI has limitations. However, VNA closely matches HI in populating these profiles. Thus, we hold that these profiles, having face- and content-validity, may provide a basis for understanding variation in genomic and transcriptomic response to influenza vaccination in this age group.

Computational Identification of Antigenicity-Associated Sites in the Hemagglutinin Protein of A/H1N1 Seasonal Influenza Virus

The antigenic variability of influenza viruses has always made influenza vaccine development challenging. The punctuated nature of antigenic drift of influenza virus suggests that a relatively small number of genetic changes or combinations of genetic changes may drive changes in antigenic phenotype. The present study aimed to identify antigenicity-associated sites in the hemagglutinin protein of A/H1N1 seasonal influenza virus using computational approaches. Random Forest Regression (RFR) and Support Vector Regression based on Recursive Feature Elimination (SVR-RFE) were applied to H1N1 seasonal influenza viruses and used to analyze the associations between amino acid changes in the HA1 polypeptide and antigenic variation based on hemagglutination-inhibition (HI) assay data. Twenty-three and twenty antigenicity-associated sites were identified by RFR and SVR-RFE, respectively, by considering the joint effects of amino acid residues on antigenic drift. Our proposed approaches were further validated with the H3N2 dataset. The prediction models developed in this study can quantitatively predict antigenic differences with high prediction accuracy based only on HA1 sequences. Application of the study results can increase understanding of H1N1 seasonal influenza virus antigenic evolution and accelerate the selection of vaccine strains.

Single hemagglutinin mutations that alter both antigenicity and receptor binding avidity influence influenza virus antigenic clustering

The hemagglutination inhibition (HAI) assay is the primary measurement used for identifying antigenically novel influenza virus strains. HAI assays measure the amount of reference sera required to prevent virus binding to red blood cells. Receptor binding avidities of viral strains are not usually taken into account when interpreting these assays. Here, we created antigenic maps of human H3N2 viruses that computationally account for variation in viral receptor binding avidities. These new antigenic maps differ qualitatively from conventional antigenic maps based on HAI measurements alone. We experimentally focused on an antigenic cluster associated with a single N145K hemagglutinin (HA) substitution that occurred between 1992 and 1995. Reverse-genetics experiments demonstrated that the N145K HA mutation increases viral receptor binding avidity. Enzyme-linked immunosorbent assays (ELISA) revealed that the N145K HA mutation does not prevent antibody binding; rather, viruses possessing this mutation escape antisera in HAI assays simply by attaching to cells more efficiently. Unexpectedly, we found an asymmetric antigenic effect of the N145K HA mutation. Once H3N2 viruses acquired K145, an epitope involving amino acid 145 became antigenically dominant. Antisera raised against an H3N2 strain possessing K145 had reduced reactivity to H3N2 strains possessing N145. Thus, individual mutations in HA can influence antigenic groupings of strains by altering receptor binding avidity and by changing the dominance of antibody responses. Our results indicate that it will be important to account for variation in viral receptor binding avidity when performing antigenic analyses in order to identify genuine antigenic differences among influenza virus variants.

Validation of the modified hemagglutination inhibition assay (mHAI), a robust and sensitive serological test for analysis of influenza virus-specific immune response

BACKGROUND

The hemagglutination inhibition assay (HAI) is universally regarded as the gold standard in influenza virus serology. Nevertheless, difficulties in titre readouts are common and interlaboratory variations are frequently reported.

OBJECTIVE

We developed and validated the modified HAI to facilitate reliable, accurate and reproducible analysis of sera derived from influenza vaccination studies.

STUDY DESIGN

Clinical and preclinical serum samples, NIBSC reference sera and seasonal influenza virus type A (H1N1 and H3N2) and type B antigens were employed to validate the mHAI. Moreover, pandemic virus strains (H5N1 and H1N1pdm09) were used to prove assay robustness.

RESULTS

Utilisation of a 0.08% solution of stabilised human erythrocytes, assay buffer containing bovine serum albumin and microscopical plate readout are the major differences between the modified and standard HAI assay protocols. Validation experiments revealed that the mHAI is linear, specific and up to eightfold more sensitive than the standard HAI. In 95.6% of all measurements mHAI titres were precisely measured irrespective of the assay day, run or operator. Moreover, 96.4% (H1N1) or 95.2% (H3N2 and B), respectively, of all serum samples were determined within one dilution step of the nominal values for spiked samples. Finally, the mHAI results remained unaffected by variations in virus antigens, erythrocytes, reagents, laboratory location, sample storage conditions or matrix components.

CONCLUSION

The modified HAI is easy to analyse, requires only a single source of erythrocytes and allows utilisation of numerous influenza virus antigens, also including virus strains which are difficult to handle by the standard HAI (e.g. H3N2, H5N1 and H1N1pdm09).

Estimating the influenza vaccine effectiveness against medically attended influenza in clinical settings: a hospital-based case-control study with a rapid diagnostic test in Japan

Background

Influenza vaccine effectiveness (VE) studies are usually conducted by specialized agencies and require time and resources. The objective of this study was to estimate the influenza VE against medically attended influenza using a test-negative case-control design with rapid influenza diagnostic tests (RIDT) in a clinical setting.

Methods

A prospective study was conducted at a community hospital in Nagasaki, western Japan during the 2010/11 influenza season. All outpatients aged 15 years and older with influenza-like illnesses (ILI) who had undergone RIDT were enrolled. A test-negative case-control design was applied to estimate the VEs: the cases were ILI patients with positive RIDT results and the controls were ILI patients with negative RIDT results. Information on patient characteristics, including vaccination histories, was collected using questionnaires and medical records.

Results

Between December 2010 and April 2011, 526 ILI patients were tested with RIDT, and 476 were eligible for the analysis. The overall VE estimate against medically attended influenza was 47.6%, after adjusting for the patients' age groups, presence of chronic conditions, month of visit, and smoking and alcohol use. The seasonal influenza vaccine reduced the risk of medically attended influenza by 60.9% for patients less than 50 years of age, but a significant reduction was not observed for patients 50 years of age and older. A sensitivity analysis provided similar figures.

Conclusion

The test-negative case-control study using RIDT provided moderate influenza VE consistent with other reports. Utilizing the commonly used RIDT to estimate VE provides rapid assessment of VE; however, it may require validation with more specific endpoint.

Alignment free characterization of the influenza-A hemagglutinin genes by the ISSCOR method

Analyses and visualizations by the ISSCOR method of the influenza virus hemagglutinin genes of three different A-subtypes revealed some rather striking temporal (for A/H3N3), and spatial relationships (for A/H5N1) between groups of individual gene subsets. The application to the A/H1N1 set revealed also relationships between the seasonal H1, and the swine-like novel 2009 H1v variants in a quick and unambiguous manner. Based on these examples we consider the application of the ISSCOR method for analysis of large sets of homologous genes as a worthwhile addition to a toolbox of genomics-it allows a rapid diagnostics of trends, and possibly can even aid an early warning of newly emerging epidemiological threats.

Characterization of the Candiru antigenic complex (Bunyaviridae: Phlebovirus), a highly diverse and reassorting group of viruses affecting humans in tropical America

The genus Phlebovirus of the family Bunyaviridae consists of approximately 70 named viruses, currently assigned to nine serocomplexes (species) based on antigenic similarities. Sixteen other named viruses that show little serologic relationship to the nine recognized groups are also classified as tentative species in the genus. In an effort to develop a more precise classification system for phleboviruses, we are attempting to sequence most of the named viruses in the genus with the goal of clarifying their phylogenetic relationships. In this report, we describe the serologic and phylogenetic relationships of 13 viruses that were found to be members of the Candiru serocomplex; 6 of them cause disease in humans. Analysis of full genome sequences revealed branching inconsistencies that suggest five reassortment events, all involving the M segment, and thus appear to be natural reassortants. This high rate of reassortment illustrates the inaccuracy of a classification system based solely on antigenic relationships.

Serological response to the 2009 pandemic influenza A (H1N1) virus for disease diagnosis and estimating the infection rate in Thai population

Background

Individuals infected with the 2009 pandemic virus A(H1N1) developed serological response which can be measured by hemagglutination-inhibition (HI) and microneutralization (microNT) assays.

Methodology/Principal Findings

MicroNT and HI assays for specific antibody to the 2009 pandemic virus were conducted in serum samples collected at the end of the first epidemic wave from various groups of Thai people: laboratory confirmed cases, blood donors and health care workers (HCW) in Bangkok and neighboring province, general population in the North and the South, as well as archival sera collected at pre- and post-vaccination from vaccinees who received influenza vaccine of the 2006 season. This study demonstrated that goose erythrocytes yielded comparable HI antibody titer as compared to turkey erythrocytes. In contrast to the standard protocol, our investigation found out the necessity to eliminate nonspecific inhibitor present in the test sera by receptor destroying enzyme (RDE) prior to performing microNT assay. The investigation in pre-pandemic serum samples showed that HI antibody was more specific to the 2009 pandemic virus than NT antibody. Based on data from pre-pandemic sera together with those from the laboratory confirmed cases, HI antibody titers ≥40 for adults and ≥20 for children could be used as the cut-off level to differentiate between the individuals with or without past infection by the 2009 pandemic virus.

Conclusions/Significance

Based on the cut-off criteria, the infection rates of 7 and 12.8% were estimated in blood donors and HCW, respectively after the first wave of the 2009 influenza pandemic. Among general population, the infection rate of 58.6% was found in children versus 3.1% in adults.

New methods for analyzing serological data with applications to influenza surveillance

Please cite this paper as: Ndifon W. (2011) New methods for analyzing serological data with applications to influenza surveillance. Influenza and Other Respiratory Viruses DOI: 10.1111/j.1750‐2659.2010.00192.x.

Background  Two important challenges to the use of serological assays for influenza surveillance include the substantial amount of experimental effort involved and the inherent noisiness of serological data.

Results  I show that log‐transformed serological data exist in an effectively one‐dimensional space. I use this result, together with new mechanistic insights into serological assays, to develop computational methods for accurately and efficiently recovering unmeasured serological data from a sample of measured data, for systematically minimizing noise and other types of non‐antigenic variation found in the data, and for quantifying and visualizing antigenic variation. The methods can also be applied to data with effective dimensionality greater than one, under certain conditions.

Conclusion  Careful application of the methods developed here would enable the collection of better‐quality serological data on a greater number of circulating influenza viruses than is currently possible and improve the ability to identify potential epidemic and pandemic viruses before they become widespread. Although the focus here is on influenza surveillance, the described methods are more widely applicable.

Signs of the 2009 influenza pandemic in the New York-Presbyterian Hospital electronic health records

Background

In June of 2009, the World Health Organization declared the first influenza pandemic of the 21^st century, and by July, New York City's New York-Presbyterian Hospital (NYPH) experienced a heavy burden of cases, attributable to a novel strain of the virus (H1N1pdm).

Methods and Results

We present the signs in the NYPH electronic health records (EHR) that distinguished the 2009 pandemic from previous seasonal influenza outbreaks via various statistical analyses. These signs include (1) an increase in the number of patients diagnosed with influenza, (2) a preponderance of influenza diagnoses outside of the normal flu season, and (3) marked vaccine failure. The NYPH EHR also reveals distinct age distributions of patients affected by seasonal influenza and the pandemic strain, and via available longitudinal data, suggests that the two may be associated with distinct sets of comorbid conditions as well. In particular, we find significantly more pandemic flu patients with diagnoses associated with asthma and underlying lung disease. We further observe that the NYPH EHR is capable of tracking diseases at a resolution as high as particular zip codes in New York City.

Conclusion

The NYPH EHR permits early detection of pandemic influenza and hypothesis generation via identification of those significantly associated illnesses. As data standards develop and databases expand, EHRs will contribute more and more to disease detection and the discovery of novel disease associations.

Correlation of influenza virus excess mortality with antigenic variation: application to rapid estimation of influenza mortality burden

The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention.

In epidemiology, investigators usually rely on surveillance data to assess the impact of an influenza virus on human health. However, accurate assessment of the influenza mortality burden at the early stage of influenza infection is rather challenging because the early influenza surveillance data are very limited and prone to bias as well. This speaks to an urgent need for the development of a more effective method for rapid and accurate estimation of influenza mortality burden. By proposing a novel host-virus interaction model, we have established a quantitative relationship between the antigenic variation of human influenza virus and its mortality burden. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. We believe that our work will help develop a timely and sensible influenza preparedness programme that balances the gains of public health with the social and economic costs.

Flexible label-free quantitative assay for antibodies to influenza virus hemagglutinins

During the initial pandemic influenza H1N1 virus outbreak, assays such as hemagglutination inhibition and microneutralization provided important information on the relative protection afforded by the population's cross-reactivity from prior infections and immunizations with seasonal vaccines. However, these assays continue to be limited in that they are difficult to automate for high throughput, such as in pandemic situations, as well as to standardize between labs. Thus, new technologies are being sought to improve standardization, reliability, and throughput by using chemically defined reagents rather than whole cells and virions. We now report the use of a cell-free and label-free flu antibody biosensor assay (f-AbBA) for influenza research and diagnostics that utilizes recombinant hemagglutinin (HA) in conjunction with label-free biolayer interferometry technology to measure biomolecular interactions between the HA and specific anti-HA antibodies or sialylated ligands. We evaluated f-AbBA to determine anti-HA antibody binding activity in serum or plasma to assess vaccine-induced humoral responses. This assay can reveal the impact of antigenic difference on antibody binding to HA and also measure binding to different subtypes of HA. We also show that the biosensor assay can measure the ability of HA to bind a model sialylated receptor-like ligand. f-AbBA could be used in global surveillance laboratories since preliminary tests on desiccated HA probes showed no loss of activity after >2 months in storage at room temperature, indicating that the same reagent lots could be used in different laboratories to minimize interlaboratory assay fluctuation. Future development of such reagents and similar technologies may offer a robust platform for future influenza surveillance activities.

The impact of matching vaccine strains and post-SARS public health efforts on reducing influenza-associated mortality among the elderly

Public health administrators do not have effective models to predict excess influenza-associated mortality and monitor viral changes associated with it. This study evaluated the effect of matching/mismatching vaccine strains, type/subtype pattern changes in Taiwan's influenza viruses, and the impact of post-SARS (severe acute respiratory syndrome) public health efforts on excess influenza-associated mortalities among the elderly. A negative binomial model was developed to estimate Taiwan's monthly influenza-associated mortality among the elderly. We calculated three winter and annual excess influenza-associated mortalities [pneumonia and influenza (P&I), respiratory and circulatory, and all-cause] from the 1999-2000 through the 2006-2007 influenza seasons. Obtaining influenza virus sequences from the months/years in which death from P&I was excessive, we investigated molecular variation in vaccine-mismatched influenza viruses by comparing hemagglutinin 1 (HA1) of the circulating and vaccine strains. We found that the higher the isolation rate of A (H3N2) and vaccine-mismatched influenza viruses, the greater the monthly P&I mortality. However, this significant positive association became negative for higher matching of A (H3N2) and public health efforts with post-SARS effect. Mean excess P&I mortality for winters was significantly higher before 2003 than after that year [mean +/- S.D.: 1.44+/-1.35 vs. 0.35+/-1.13, p = 0.04]. Further analysis revealed that vaccine-matched circulating influenza A viruses were significantly associated with lower excess P&I mortality during post-SARS winters (i.e., 2005-2007) than during pre-SARS winters [0.03+/-0.06 vs. 1.57+/-1.27, p = 0.01]. Stratification of these vaccine-matching and post-SARS effect showed substantial trends toward lower elderly excess P&I mortalities in winters with either mismatching vaccines during the post-SARS period or matching vaccines during the pre-SARS period. Importantly, all three excess mortalities were at their highest in May, 2003, when inter-hospital nosocomial infections were peaking. Furthermore, vaccine-mismatched H3N2 viruses circulating in the years with high excess P&I mortality exhibited both a lower amino acid identity percentage of HA1 between vaccine and circulating strains and a higher numbers of variations at epitope B. Our model can help future decision makers to estimate excess P&I mortality effectively, select and test virus strains for antigenic variation, and evaluate public health strategy effectiveness.

A computational analysis of the antigenic properties of haemagglutinin in influenza A H3N2

MOTIVATION

Modelling antigenic shift in influenza A H3N2 can help to predict the efficiency of vaccines. The virus is known to exhibit sudden jumps in antigenic distance, and prediction of such novel strains from amino acid sequence differences remains a challenge.

RESULTS

From analysis of 6624 amino acid sequences of wild-type H3, we propose updates to the frequently referenced list of 131 amino acids located at or near the five identified antibody binding regions in haemagglutinin (HA). We introduce a class of predictive models based on the analysis of amino acid changes in these binding regions, and extend the principle to changes in HA1 as a whole by dividing the molecule into regional bands. Our results show that a range of simple models based on banded changes give better predictive performance than models based on the established five canonical regions and can identify a higher proportion of vaccine escape candidates among novel strains than a current state-of-the-art model.

Prescription of anti-influenza drugs for healthy adults: a systematic review and meta-analysis

In publicly funded health systems with finite resources, management decisions are based on assessments of clinical effectiveness and cost-effectiveness. The UK National Institute for Health and Clinical Excellence commissioned a systematic review to inform their 2009 update to guidance on the use of antiviral drugs for the treatment of influenza. We searched databases for studies of the use of neuraminidase inhibitors for the treatment of seasonal influenza. We present the results for healthy adults (ie, adults without known comorbidities) and people at-risk of influenza-related complications. There was an overall reduction in the median time to symptom alleviation in healthy adults by 0.57 days (95% CI -1.07 to -0.08; p=0.02; 2701 individuals) with zanamivir, and 0.55 days (95% CI -0.96 to -0.14; p=0.008; 1410 individuals) with oseltamivir. In those at risk, the median time to symptom alleviation was reduced by 0.98 days (95% CI -1.84 to -0.11; p=0.03; 1252 individuals) with zanamivir, and 0.74 days (95% CI -1.51 to 0.02; p=0.06; 1472 individuals) with oseltamivir. Little information was available on the incidence of complications. In view of the advantages and disadvantages of different management strategies for controlling seasonal influenza in healthy adults recommending the use of antiviral drugs for the treatment of people presenting with symptoms is unlikely to be the most appropriate course of action.

Differential neutralization efficiency of hemagglutinin epitopes, antibody interference, and the design of influenza vaccines

It is generally assumed that amino acid mutations in the surface protein, hemagglutinin (HA), of influenza viruses allow these viruses to circumvent neutralization by antibodies induced during infection. However, empirical data on circulating influenza viruses show that certain amino acid changes to HA actually increase the efficiency of neutralization of the mutated virus by antibodies raised against the parent virus. Here, we suggest that this surprising increase in neutralization efficiency after HA mutation could reflect steric interference between antibodies. Specifically, if there is a steric competition for binding to HA by antibodies with different neutralization efficiencies, then a mutation that reduces the binding of antibodies with low neutralization efficiencies could increase overall viral neutralization. We use a mathematical model of virus-antibody interaction to elucidate the conditions under which amino acid mutations to HA could lead to an increase in viral neutralization. Using insights gained from the model, together with genetic and structural data, we predict that amino acid mutations to epitopes C and E of the HA of influenza A/H3N2 viruses could lead on average to an increase in the neutralization of the mutated viruses. We present data supporting this prediction and discuss the implications for the design of more effective vaccines against influenza viruses and other pathogens.