In Silico Vaccine Strain Prediction for Human Influenza Viruses

R229I substitution from oseltamivir induction in HA1 region significantly increased the fitness of a H7N9 virus bearing NA 292K

The proportion of human isolates with reduced neuraminidase inhibitors (NAIs) susceptibility in highly pathogenic avian influenza (HPAI) H7N9 virus was high. These drug-resistant strains showed good replication capacity without serious loss of fitness. In the presence of oseltamivir, R229I substitution were found in HA1 region of the HPAI H7N9 virus before NA R292K appeared. HPAI H7N9 or H7N9/PR8 recombinant viruses were developed to study whether HA R229I could increase the fitness of the H7N9 virus bearing NA 292K. Replication efficiency was assessed in MDCK or A549 cells. Neuraminidase enzyme activity and receptor-binding ability were analyzed. Pathogenicity in C57 mice was evaluated. Antigenicity analysis was conducted through a two-way HI test, in which the antiserum was obtained from immunized ferrets. Transcriptomic analysis of MDCK infected with HPAI H7N9 24hpi was done. It turned out that HA R229I substitution from oseltamivir induction in HA1 region increased (1) replication ability in MDCK(P < 0.05) and A549(P < 0.05), (2) neuraminidase enzyme activity, (3) binding ability to both α2,3 and α2,6 receptor, (4) pathogenicity to mice(more weight loss; shorter mean survival day; viral titer in respiratory tract, P < 0.05; Pathological changes in pneumonia), (5) transcriptome response of MDCK, of the H7N9 virus bearing NA 292K. Besides, HA R229I substitution changed the antigenicity of H7N9/PR8 virus (>4-fold difference of HI titre). It indicated that through the fine-tuning of HA-NA balance, R229I increased the fitness and changed the antigenicity of H7N9 virus bearing NA 292K. Public health attention to this mechanism needs to be drawn.

Seasonal antigenic prediction of influenza A H3N2 using machine learning

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

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.

Computational design and evaluation of mRNA- and protein-based conjugate vaccines for influenza A and SARS-CoV-2 viruses

BACKGROUND

Israel confirmed the first case of "flurona"-a co-infection of seasonal flu (IAV) and SARS-CoV-2 in an unvaccinated pregnant woman. This twindemic has been confirmed in multiple countries and underscores the importance of managing respiratory viral illnesses.

RESULTS

The novel conjugate vaccine was designed by joining four hemagglutinin, three neuraminidase, and four S protein of B-cell epitopes, two hemagglutinin, three neuraminidase, and four S proteins of MHC-I epitopes, and three hemagglutinin, nine neuraminidase, and five S proteins of MHC-II epitopes with linkers and adjuvants. The constructed conjugate vaccine was found stable, non-toxic, non-allergic, and antigenic with 0.6466 scores. The vaccine contained 14.87% alpha helix, 29.85% extended strand, 9.64% beta-turn, and 45.64% random coil, which was modeled to a 3D structure with 94.7% residues in the most favored region of the Ramachandran plot and Z-score of -3.33. The molecular docking of the vaccine with TLR3 represented -1513.9 kcal/mol of binding energy with 39 hydrogen bonds and 514 non-bonded contacts, and 1.582925e-07 of eigenvalue complex. Immune stimulation prediction showed the conjugate vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies.

CONCLUSION

The in silico-designed vaccine against IAV and SARS-CoV-2 showed good population coverage and immune response with predicted T- and B-cell epitopes, favorable molecular docking, Ramachandran plot results, and good protein expression. It fulfilled safety criteria, indicating potential for preclinical studies and experimental clinical trials.

Advances in Adjuvanted Influenza Vaccines

The numerous influenza infections that occur every year present a major public health problem. Influenza vaccines are important for the prevention of the disease; however, their effectiveness against infection can be suboptimal. Particularly in the elderly, immune induction can be insufficient, and the vaccine efficacy against infection is usually lower than that in young adults. Vaccine efficacy can be improved by the addition of adjuvants, and an influenza vaccine with an oil-in-water adjuvant MF59, FLUAD, has been recently licensed in the United States and other countries for persons aged 65 years and older. Although the adverse effects of adjuvanted vaccines have been a concern, many adverse effects of currently approved adjuvanted influenza vaccines are mild and acceptable, given the overriding benefits of the vaccine. Since sufficient immunity can be induced with a small amount of vaccine antigen in the presence of an adjuvant, adjuvanted vaccines promote dose sparing and the prompt preparation of vaccines for pandemic influenza. Adjuvants not only enhance the immune response to antigens but can also be effective against antigenically different viruses. In this narrative review, we provide an overview of influenza vaccines, both past and present, before presenting a discussion of adjuvanted influenza vaccines and their future.

Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1) pdm09 viruses

The Influenza A (H1N1) pdm09 virus caused a global pandemic in 2009 and has circulated seasonally ever since. As the continual genetic evolution of hemagglutinin in this virus leads to antigenic drift, rapid identification of antigenic variants and characterization of the antigenic evolution are needed. In this study, we developed PREDAC-H1pdm, a model to predict antigenic relationships between H1N1pdm viruses and identify antigenic clusters for post-2009 pandemic H1N1 strains. Our model performed well in predicting antigenic variants, which was helpful in influenza surveillance. By mapping the antigenic clusters for H1N1pdm, we found that substitutions on the Sa epitope were common for H1N1pdm, whereas for the former seasonal H1N1, substitutions on the Sb epitope were more common in antigenic evolution. Additionally, the localized epidemic pattern of H1N1pdm was more obvious than that of the former seasonal H1N1, which could make vaccine recommendation more sophisticated. Overall, the antigenic relationship prediction model we developed provides a rapid determination method for identifying antigenic variants, and the further analysis of evolutionary and epidemic characteristics can facilitate vaccine recommendations and influenza surveillance for H1N1pdm.

[Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype H3N2]

INTRODUCTION

The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data.

MATERIALS AND METHODS

In this study, a mathematical model was proposed, based on finding, using regression analysis, the dependence of HAI titers on substitutions in antigenic sites of sequences. The computer program we developed can process data (GISAID, NCBI, etc.) and create real-time databases according to the set tasks.

RESULTS

Based on our research, an additional antigenic site F was identified. The difference in 1.6 times the adjusted R2, on subsets of viruses grown in cell culture and grown in chicken embryos, demonstrates the validity of our decision to divide the original data array by passage histories. We have introduced the concept of a degree of homology between two arbitrary strains, which takes the value of a function depending on the Hamming distance, and it has been shown that the regression results significantly depend on the choice of function. The provided analysis showed that the most significant antigenic sites are A, B, and E. The obtained results on predicted HAI titers showed a good enough result, comparable to similar work by our colleagues.

CONCLUSION

The proposed method could serve as a useful tool for future forecasts, with further study to confirm its sustainability.

Designing multi-epitope mRNA construct as a universal influenza vaccine candidate for future epidemic/pandemic preparedness

Despite the availability of prevention and treatment strategies and advancing immunization approaches, the influenza virus remains a global threat that continues to plague humanity with unpredictable pandemics. Due to the unusual genetic variability and segmented genome, the reassortment between different strains of influenza is facilitated and the viruses continuously evolve and adapt to the host cell's immunity. This underlies the seasonal vaccine mismatches that decrease the vaccine efficacy and increase the risk of outbreaks. Thus, the development of a universal vaccine covering all the influenza A and B strains would reduce the pervasiveness of the influenza virus. In the current study, a potentially universal influenza multi-epitope vaccine was designed based on the experimentally tested conserved T cell and B cell epitopes of hemagglutinin (HA), neuraminidase (NA), nucleoprotein (NP), and matrix-2 proton channel (M2) of the virus. The immune simulation and molecular docking of the vaccine construct with TLR2, TLR3, and TLR4 elicited the favorable immunogenicity of the vaccine and the formation of stable complexes, respectively. Ultimately, based on the immunoinformatics analysis, the universal mRNA multi-epitope vaccine designed in this study might have a protection potential against the various subtypes of influenza A and B.

Immunoinformatics Approach for Epitope Mapping of Immunogenic Regions (N, F and H Gene) of Small Ruminant Morbillivirus and Its Comparative Analysis with Standard Vaccinal Strains for Effective Vaccine Development

BACKGROUND

Outbreaks of small ruminant morbillivirus (SRMV) are regularly occurring in Pakistan despite vaccine availability. This study was designed to identify substitutions within the immunogenic structural and functional regions of the nucleocapsid, fusion, and hemagglutinin genes of SRMV and their comparison with vaccinal strains of Nigerian and Indian origin.

METHODS

Swabs and tissue samples were collected from diseased animals. RT-PCR was used to characterize selected genes encoded by viral RNA. The study's N, F, and H protein sequences and vaccinal strains were analyzed for B and T cell epitope prediction using ABCpred, Bipred, and IEDB, respectively.

RESULTS

Significant substitutions were found on the C terminus of the nucleocapsid, within the fusion motif region of the fusion gene and in the immunoreactive region of the hemagglutinin gene.

CONCLUSION

Our results emphasize the need for the development of effective vaccines that match the existing variants of SRMV strains circulating in Pakistan.

Reduced amino acid alphabet-based encoding and its impact on modeling influenza antigenic evolution

Currently, vaccination is one of the most efficient ways to control and prevent influenza infection. Vaccine production largely relies on the results of laboratory assays, including hemagglutination inhibition and microneutralization assays, which are time-consuming and laborious. Viruses can escape from the immune response that results in the need to revise and update vaccines biannually. The hemagglutination inhibition assay can measure how effectively antibodies against a reference strain bind and block an antigen of the test strain. Various computer-aided models have been developed to optimize candidate vaccine strain selection. A general problem in modeling of antigenic evolution is the representation of genetic sequences for input into the research model. Our motivation stems from the well-known problem of encoding genetic information for modeling antigenic evolution. This paper introduces a two-fold encoding approach based on reduced amino acid alphabet and amino acid index databases called AAindex. We propose to apply a simplified amino acid alphabet in modeling of antigenic evolution. A simplified alphabet, also called a sub-alphabet or reduced amino acid alphabet, implies to use the 20 amino acids being clustered and divided into amino acid groups. The proposed encoding allows to redefine mutations termed for amino acid groups located in reduced alphabets. We investigated 40 reduced amino acid sets and their performance in modeling antigenic evolution. The experimental results indicate that the proposed reduced amino acid alphabets can achieve the performance of the standard alphabet in its accuracy. Moreover, these alphabets provide deeper insight into various aspects of the relationship between mutation and antigenic variation. By checking identified high-impact sites in the Influenza Research Database, we found that not only antigenic sites have a significant influence on antigenicity, but also other amino acids located in close proximity. The results indicate that all selected non-antigenic sites are related to immune responses. According to the Influenza Research Database, these have been experimentally determined to be T-cell epitopes, B-cell epitopes, and MHC-binding epitopes of different classes. This highlighted a caveat: while simulating antigenic evolution, the model should consider not only the genetic information on antigenic sites, but also that of neighboring positions, as they may indirectly impact antigenicity. Additionally, our findings indicate that structural and charge characteristics are the most beneficial in modeling antigenic evolution, which is in agreement with previous studies.

Promising strategy for developing mRNA-based universal influenza virus vaccine for human population, poultry, and pigs- focus on the bigger picture

Since the first outbreak in the 19th century influenza virus has remained emergent owing to the huge pandemic potential. Only the pandemic of 1918 caused more deaths than any war in world history. Although two types of influenza- A (IAV) and B (IBV) cause epidemics annually, influenza A deserves more attention as its nature is much wilier. IAVs have a large animal reservoir and cause the infection manifestation not only in the human population but in poultry and domestic pigs as well. This many-sided characteristic of IAV along with the segmented genome gives rise to the antigenic drift and shift that allows evolving the new strains and new subtypes, respectively. As a result, the immune system of the body is unable to recognize them. Importantly, several highly pathogenic avian IAVs have already caused sporadic human infections with a high fatality rate (~60%). The current review discusses the promising strategy of using a potentially universal IAV mRNA vaccine based on conserved elements for humans, poultry, and pigs. This will better aid in averting the outbreaks in different susceptible species, thus, reduce the adverse impact on agriculture, and economics, and ultimately, prevent deadly pandemics in the human population.

Comparative molecular dynamics study of the receptor-binding domains in SARS-CoV-2 and SARS-CoV and the effects of mutations on the binding affinity

Here, we report on a computational comparison of the receptor-binding domains (RBDs) on the spike proteins of severe respiratory syndrome coronavirus-2 (SARS-CoV-2) and SARS-CoV in free forms and as complexes with angiotensin-converting enzyme 2 (ACE2) as their receptor in humans. The impact of 42 mutations discovered so far on the structure and thermodynamics of SARS-CoV-2 RBD was also assessed. The binding affinity of SARS-CoV-2 RBD for ACE2 is higher than that of SARS-CoV RBD. The binding of COVA2-04 antibody to SARS-CoV-2 RBD is more energetically favorable than the binding of COVA2-39, but also less favorable than the formation of SARS-CoV-2 RBD-ACE2 complex. The net charge, the dipole moment and hydrophilicity of SARS-CoV-2 RBD are higher than those of SARS-CoV RBD, producing lower solvation and surface free energies and thus lower stability. The structure of SARS-CoV-2 RBD is also more flexible and more open, with a larger solvent-accessible surface area than that of SARS-CoV RBD. Single-point mutations have a dramatic effect on distribution of charges, most prominently at the site of substitution and its immediate vicinity. These charge alterations alter the free energy landscape, while X→F mutations exhibit a stabilizing effect on the RBD structure through π stacking. F456 and W436 emerge as two key residues governing the stability and affinity of the spike protein for its ACE2 receptor. These analyses of the structural differences and the impact of mutations on different viral strains and members of the coronavirus genera are an essential aid in the development of effective therapeutic strategies. Communicated by Ramaswamy H. Sarma.

HA gene amino acid mutations contribute to antigenic variation and immune escape of H9N2 influenza virus

Based on differences in the amino acid sequence of the protein haemagglutinin (HA), the H9N2 avian influenza virus (H9N2 virus) has been clustered into multiple lineages, and its rapidly ongoing evolution increases the difficulties faced by prevention and control programs. The HA protein, a major antigenic protein, and the amino acid mutations that alter viral antigenicity in particular have always been of interest. Likewise, it has been well documented that some amino acid mutations in HA alter viral antigenicity in the H9N2 virus, but little has been reported regarding how these antibody escape mutations affect antigenic variation. In this study, we were able to identify 15 HA mutations that were potentially relevant to viral antigenic drift, and we also found that a key amino acid mutation, A180V, at position 180 in HA (the numbering for mature H9 HA), the only site of the receptor binding sites that is not conserved, was directly responsible for viral antigenic variation. Moreover, the recombinant virus with alanine to valine substitution at position 180 in HA in the SH/F/98 backbone (rF/HA_A180V virus) showed poor cross-reactivity to immune sera from animals immunized with the SH/F/98 (F/98, A180), SD/SS/94 (A180), JS/Y618/12 (T180), and rF/HA_A180V (V180) viruses by microneutralization (MN) assay. The A180V substitution in the parent virus caused a significant decrease in cross-MN titres by enhancing the receptor binding activity, but it did not physically prevent antibody (Ab) binding. The strong receptor binding avidity prevented viral release from cells. Moreover, the A180V substitution promoted H9N2 virus escape from an in vitro pAb-neutralizing reaction, which also slightly affected the cross-protection in vivo. Our results suggest that the A180V mutation with a strong receptor binding avidity contributed to the low reactors in MN/HI assays and slightly affected vaccine efficacy but was not directly responsible for immune escape, which suggested that the A180V mutation might play a key role in the process of the adaptive evolution of H9N2 virus.

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.

Modeling the selective advantage of new amino acids on the hemagglutinin of H1N1 influenza viruses using their patient age distributions

In 2009, a new strain of H1N1 influenza A virus caused a pandemic, and its descendant strains are causing seasonal epidemics worldwide. Given the high mutation rate of influenza viruses, variant strains having different amino acids on hemagglutinin (HA) continuously emerge. To prepare vaccine strains for the next influenza seasons, it is an urgent task to predict which variants will be selected in the viral population. An analysis of 24,681 pairs of an amino acid sequence of HA of H1N1pdm2009 viruses and its patient age showed that the empirical fixation probability of new amino acids on HA significantly differed depending on their frequencies in the population, patient age distributions, and epitope flags. The selective advantage of a variant strain having a new amino acid was modeled by linear combinations of patients age distributions and epitope flags, and then the fixation probability of the new amino acid was modeled using Kimura’s formula for advantageous selection. The parameters of models were estimated from the sequence data and models were tested with four-fold cross validations. The frequency of new amino acids alone can achieve high sensitivity, specificity, and precision in predicting the fixation of a new amino acid of which frequency is more than 0.11. The estimated parameter suggested that viruses with a new amino acid having a frequency in the population higher than 0.11 have a significantly higher selective advantage compared to viruses with the old amino acid at the same position. The model considering the Z-value of patient age rank-sums of new amino acids predicted amino acid substitutions on HA with a sensitivity of 0.78, specificity of 0.86, and precision of 0.83, showing significant improvement compared to the constant selective advantage model, which used only the frequency of the amino acid. These results suggested that H1N1 viruses tend to be selected in the adult population, and frequency of viruses having new amino acids and their patient ages are useful to predict amino acid substitutions on HA.

Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks

Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection. By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the recommendation of the influenza vaccines using recurrent neural networks (RNNs). The Encoder-decoder architecture of RNN model enables us to perform sequence-to-sequence prediction. We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and recommended vaccines is greater than 98% and the Pepitope<0.2 indicates their antigenic similarity. The multi-step vaccine prediction further demonstrates the robustness of our method which achieves comparable results in contrast to single step prediction. The results show significant matches of the recommended vaccine strains to the circulating strains. We believe it would facilitate the process of vaccine selection and surveillance of seasonal influenza epidemics.

Limited Predictability of Amino Acid Substitutions in Seasonal Influenza Viruses

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

In silico prediction of influenza vaccine effectiveness by sequence analysis

The effectiveness of seasonal influenza vaccines varies with the matching of vaccine strains to circulating strains. Based on the genetic distance of hemagglutinin and neuraminidase gene of the influenza viruses to vaccine strains, we statistically quantified the relationship between the genetic mismatch and vaccine effectiveness (VE) for influenza A/H1N1pdm09, A/H3N2 and B. We also proposed a systematic approach to integrate multiple genes and influenza types for overall VE estimation. Evident linear relationships were identified and validated in independent data. The modelling framework may enable in silico prediction for VE on a real-time basis and inform the influenza vaccine selection strategy.

Identification of Hidden Population Structure in Time-Scaled Phylogenies

Population structure influences genealogical patterns, however, data pertaining to how populations are structured are often unavailable or not directly observable. Inference of population structure is highly important in molecular epidemiology where pathogen phylogenetics is increasingly used to infer transmission patterns and detect outbreaks. Discrepancies between observed and idealized genealogies, such as those generated by the coalescent process, can be quantified, and where significant differences occur, may reveal the action of natural selection, host population structure, or other demographic and epidemiological heterogeneities. We have developed a fast non-parametric statistical test for detection of cryptic population structure in time-scaled phylogenetic trees. The test is based on contrasting estimated phylogenies with the theoretically expected phylodynamic ordering of common ancestors in two clades within a coalescent framework. These statistical tests have also motivated the development of algorithms which can be used to quickly screen a phylogenetic tree for clades which are likely to share a distinct demographic or epidemiological history. Epidemiological applications include identification of outbreaks in vulnerable host populations or rapid expansion of genotypes with a fitness advantage. To demonstrate the utility of these methods for outbreak detection, we applied the new methods to large phylogenies reconstructed from thousands of HIV-1 partial pol sequences. This revealed the presence of clades which had grown rapidly in the recent past and was significantly concentrated in young men, suggesting recent and rapid transmission in that group. Furthermore, to demonstrate the utility of these methods for the study of antimicrobial resistance, we applied the new methods to a large phylogeny reconstructed from whole genome Neisseria gonorrhoeae sequences. We find that population structure detected using these methods closely overlaps with the appearance and expansion of mutations conferring antimicrobial resistance. [Antimicrobial resistance; coalescent; HIV; population structure.].

Ancestral origin, antigenic resemblance and epidemiological insights of novel coronavirus (SARS-CoV-2): Global burden and Bangladesh perspective

SARS-CoV-2, a new coronavirus strain responsible for COVID-19, has emerged in Wuhan City, China, and continuing its global pandemic nature. The availability of the complete gene sequences of the virus helps to know about the origin and molecular characteristics of this virus. In the present study, we performed bioinformatic analysis of the available gene sequence data of SARS-CoV-2 for the understanding of evolution and molecular characteristics and immunogenic resemblance of the circulating viruses. Phylogenetic analysis was performed for four types of representative viral proteins (spike, membrane, envelope and nucleoprotein) of SARS-CoV-2, HCoV-229E, HCoV-OC43, SARS-CoV, HCoV-NL63, HKU1, MERS-CoV, HKU4, HKU5 and BufCoV-HKU26. The findings demonstrated that SARS-CoV-2 exhibited convergent evolutionary relation with previously reported SARS-CoV. It was also depicted that SARS-CoV-2 proteins were highly similar and identical to SARS-CoV proteins, though proteins from other coronaviruses showed a lower level of resemblance. The cross-checked conservancy analysis of SARS-CoV-2 antigenic epitopes showed significant conservancy with antigenic epitopes derived from SARS-CoV. Descriptive epidemiological analysis on several epidemiological indices was performed on available epidemiological outbreak information from several open databases on COVID-19 (SARS-CoV-2). Satellite-derived imaging data have been employed to understand the role of temperature in the environmental persistence of the virus. Findings of the descriptive analysis were used to describe the global impact of newly emerged SARS-CoV-2, and the risk of an epidemic in Bangladesh.

L226Q Mutation on Influenza H7N9 Virus Hemagglutinin Increases Receptor-Binding Avidity and Leads to Biased Antigenicity Evaluation

Since the first outbreak in 2013, the influenza A (H7N9) virus has continued emerging and has caused over five epidemic waves. Suspected antigenic changes of the H7N9 virus based on hemagglutination inhibition (HI) assay during the fifth outbreak have prompted the update of H7N9 candidate vaccine viruses (CVVs). In this study, we comprehensively compared the serological cross-reactivities induced by the hemagglutinins (HAs) of the earlier CVV A/Anhui/1/2013 (H7/AH13) and the updated A/Guangdong/17SF003/2016 (H7/GD16). We found that although H7/GD16 showed poor HI cross-reactivity to immune sera from mice and rhesus macaques vaccinated with either H7/AH13 or H7/GD16, the cross-reactive neutralizing antibodies between H7/AH13 and H7/GD16 were comparably high. Passive transfer of H7/AH13 immune sera also provided complete protection against the lethal challenge of H7N9/GD16 virus in mice. Analysis of amino acid mutations in the HAs between H7/AH13 and H7/GD16 revealed that L226Q substitution increases the HA binding avidity to sialic acid receptors on red blood cells, leading to decreased HI titers against viruses containing HA Q226 and thus resulting in a biased antigenic evaluation based on HI assay. These results suggest that amino acids located in the receptor-binding site could mislead the evaluation of antigenic variation by solely impacting the receptor-binding avidity to red blood cells without genuine contribution to antigenic drift. Our study highlighted that viral receptor-binding avidity and combination of multiple serological assays should be taken into consideration in evaluating and selecting a candidate vaccine virus of H7N9 and other subtypes of influenza viruses.IMPORTANCE The HI assay is a standard method for profiling the antigenic characterization of influenza viruses. Suspected antigenic changes based on HI divergency in H7N9 viruses during the 2016-2017 wave prompted the recommendation of new H7N9 candidate vaccine viruses (CVVs). In this study, we found that the L226Q substitution in HA of A/Guangdong/17SF003/2016 (H7/GD16) increased the viral receptor-binding avidity to red blood cells with no impact on the antigenicity of H7N9 virus. Although immune sera raised by an earlier vaccine strain (H7/AH13) showed poor HI titers against H7/GD16, the H7/AH13 immune sera had potent cross-neutralizing antibody titers against H7/GD16 and could provide complete passive protection against H7N9/GD16 virus challenge in mice. Our study highlights that receptor-binding avidity might lead to biased antigenic evaluation by using the HI assay. Other serological assays, such as the microneutralization (MN) assay, should be considered a complementary indicator for analysis of antigenic variation and selection of influenza CVVs.

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.

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.

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.

Population Genomics of GII.4 Noroviruses Reveal Complex Diversification and New Antigenic Sites Involved in the Emergence of Pandemic Strains

Noroviruses are an important cause of viral gastroenteritis around the world. An obstacle delaying the development of norovirus vaccines is inadequate understanding of the role of norovirus diversity in immunity. Using a population genomics approach, we identified new residues on the viral capsid protein (VP1) from GII.4 noroviruses, the predominant genotype, that appear to be involved in the emergence and antigenic topology of GII.4 variants. Careful monitoring of the substitutions in those residues involved in the diversification and emergence of new viruses could help in the early detection of future novel variants with pandemic potential. Therefore, this novel information on the antigenic diversification could facilitate GII.4 norovirus vaccine design.

GII.4 noroviruses are a major cause of acute gastroenteritis. Their dominance has been partially explained by the continuous emergence of antigenically distinct variants. To gain insights into the mechanisms of viral emergence and population dynamics of GII.4 noroviruses, we performed large-scale genomics, structural, and mutational analyses of the viral capsid protein (VP1). GII.4 noroviruses exhibited a periodic replacement of predominant variants with accumulation of amino acid substitutions. Genomic analyses revealed (i) a large proportion (87%) of conserved residues; (ii) variable residues that map on the previously determined antigenic sites; and (iii) variable residues that map outside the antigenic sites. Residues in the third pattern category formed motifs on the surface of VP1, which suggested extensions of previously predicted and new uncharacterized antigenic sites. The role of two motifs (C and G) in the antigenic makeup of the GII.4 capsid protein was confirmed with monoclonal antibodies and carbohydrate blocking assays. Amino acid profiles from antigenic sites (A, C, D, E, and G) correlated with the circulation patterns of GII.4 variants, with three of them (A, C, and G) containing residues (352, 357, 368, and 378) linked with the diversifying selective pressure on the emergence of new GII.4 variants. Notably, the emergence of each variant was followed by stochastic diversification with minimal changes that did not progress toward the next variant. This report provides a methodological framework for antigenic characterization of viruses and expands our understanding of the dynamics of GII.4 noroviruses and could facilitate the design of cross-reactive vaccines.

In silico analysis for development of epitopes-based peptide vaccine against Alkhurma hemorrhagic fever virus

Alkhurma hemorrhagic fever virus (ALKV) causes a fatal clinical disease in human beings of different tropical and sub-tropical regions. Recently, the ALKV epidemics have raised a great public health concern with the room for improvement in the essential therapeutic interventions. Despite increased realistic clinical cases of ALKV infection, the efficient vaccine or immunotherapy is not yet available to-date. Therefore, the current study aimed to analyze the envelope glycoprotein of ALKV for the development of B-cells and T-cells epitope-based peptide vaccine using the computational in silico method. Utilizing various immunoinformatics approaches, a total of 5 B-cells and 25 T-cells (MHC-I = 17, MHC-II = 8) epitope-based peptides were predicted in the current study. All predicted peptides had highest antigenicity and immunogenicity scores along with high binding affinity to human leukocyte antigen (HLA) class II alleles. Among 25T-cell epitopes, three peptides were found alike to have affinity to bind both MHC-I and MHC-II alleles. These outcomes suggested that these predicted epitopes could potentially be used in the development of an efficient vaccine against ALKV, which may enable to elicit both humoral and cell-mediated immunity. Although, these predicted peptides could be useful in designing a candidate vaccine for the prevention of ALKV; however, it's in vitro and in vivo assessments are prerequisite.Communicated by Ramaswamy H. Sarma.

Predicting Antigenicity of Influenza A Viruses Using biophysical ideas

Antigenic variations of influenza A viruses are induced by genomic mutation in their trans-membrane protein HA1, eliciting viral escape from neutralization by antibodies generated in prior infections or vaccinations. Prediction of antigenic relationships among influenza viruses is useful for designing (or updating the existing) influenza vaccines, provides important insights into the evolutionary mechanisms underpinning viral antigenic variations, and helps to understand viral epidemiology. In this study, we present a simple and physically interpretable model that can predict antigenic relationships among influenza A viruses, based on biophysical ideas, using both genomic amino acid sequences and experimental antigenic data. We demonstrate the applicability of the model using a benchmark dataset of four subtypes of influenza A (H1N1, H3N2, H5N1, and H9N2) viruses and report on its performance profiles. Additionally, analysis of the model’s parameters confirms several observations that are consistent with the findings of other previous studies, for which we provide plausible explanations.

Computational Approaches and Challenges to Developing Universal Influenza Vaccines

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

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

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

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.

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

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

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

Monitoring changes in influenza A virus genomes is crucial to understand its rapid evolution and adaptation to changing conditions e.g. establishment within novel host species. Selective sweeps represent a rapid mode of adaptation and are typically observed in human influenza A viruses. We describe Sweep Dynamics (SD) plots, a computational method combining phylogenetic algorithms with statistical techniques to characterize the molecular adaptation of rapidly evolving viruses from longitudinal sequence data. SD plots facilitate the identification of selective sweeps, the time periods in which these occurred and associated changes providing a selective advantage to the virus. We studied the past genome-wide adaptation of the 2009 pandemic H1N1 influenza A (pH1N1) and seasonal H3N2 influenza A (sH3N2) viruses. The pH1N1 influenza virus showed simultaneous amino acid changes in various proteins, particularly in seasons of high pH1N1 activity. Partially, these changes resulted in functional alterations facilitating sustained human-to-human transmission. In the evolution of sH3N2 influenza viruses, we detected changes characterizing vaccine strains, which were occasionally revealed in selective sweeps one season prior to the WHO recommendation. Taken together, SD plots allow monitoring and characterizing the adaptive evolution of influenza A viruses by identifying selective sweeps and their associated signatures.