Analysis of protein modifications is critical for quality control of therapeutic biologics. However, the identification and quantification of naturally occurring glycation of membrane proteins by mass spectrometry remain technically challenging. We used highly sensitive LC MS/MS analyses combined with multiple enzyme digestions to determine low abundance early-stage lysine glycation products of influenza vaccines derived from embryonated chicken eggs and cultured cells. Straightforward sequencing was enhanced by MS/MS fragmentation of small peptides. As a result, we determined a widespread distribution of lysine modifications attributed by the region-selectivity and site-specificity of glycation toward influenza matrix 1, hemagglutinin and neuraminidase. Topological analysis provides insights into the site-specific lysine glycation, localizing in the distinct structural regions of proteins surrounding the viral envelope membrane. Our finding highlights the proteome-wide discovery of lysine glycation of influenza membrane proteins and potential effects on the structural assembly, stability, receptor binding and enzyme activity, demonstrating that the impacts of accumulated glycation on the quality of products can be directly monitored by mass spectrometry-based structural proteomics analyses.

The baculovirus expression vector system (BEVS) is a powerful tool in pharmaceutical biotechnology to infect insect cells and produce the recombinant proteins of interest. It has been well documented that optimizing the culture condition and its supplementation through designed experiments is critical for maximum protein production. In this study, besides physicochemical parameters including incubation temperature, cell count of infection, multiplicity of infection, and feeding percentage, potential supplementary factors such as cholesterol, polyamine, galactose, pluronic-F68, glucose, L-glutamine, and ZnSO4 were screened for Spodoptera frugiperda (Sf9) cell culture and expression of hemagglutinin (HA) protein of Influenza virus via Placket-Burman design and then optimized through Box-Behnken approach. The optimized conditions were then applied for scale-up culture and the expressed r-HA protein was characterized. Optimization of selected parameters via the Box-Behnken approach indicated that feed percentage, cell count, and multiplicity of infection are the main parameters affecting r-HA expression level and potency compared to the previously established culture condition. This study demonstrated the effectiveness of designing experiments to select and optimize important parameters that potentially affect Sf9 cell culture, r-HA expression, and its potency in the BEVS system.

The detection of evolutionary transitions in influenza A (H3N2) viruses\' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman\'s index, indicative of the virus\'s cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD\'s efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.

In 2004, the equine-origin H3N8 canine influenza virus (CIV) first caused an outbreak with lethal cases in racing greyhounds in Florida, USA, and then spread to domestic dogs nationwide. Although transmission of this canine virus to humans has not been reported, it is important to evaluate its zoonotic potential because of the high contact opportunities between companion dogs and humans. To gain insight into the interspecies transmissibility of H3N8 CIV, we tested its adaptability to human respiratory A549 cells through successive passages. We found that CIV acquired high growth properties in these cells mainly through mutations in surface glycoproteins, such as hemagglutinin (HA) and neuraminidase (NA). Our reverse genetics approach revealed that HA2-K82E, HA2-R163K, and NA-S18L mutations were responsible for the increased growth of CIV in human cells. Molecular analyses revealed that both HA2 mutations altered the optimum pH for HA membrane fusion activity and that the NA mutation changed the HA-NA functional balance. These findings suggest that H3N8 CIV could evolve into a human pathogen with pandemic potential through a small number of mutations, thereby posing a threat to public health in the future.

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.

BACKGROUND: Non-pharmaceutical measures and travel restrictions have halted the spread of coronavirus disease 2019 (COVID-19) and influenza. Nonetheless, with COVID-19 restrictions lifted, an unanticipated outbreak of the influenza B/Victoria virus in late 2021 and another influenza H3N2 outbreak in mid-2022 occurred in Guangdong, southern China. The mechanism underlying this phenomenon remains unknown. To better prepare for potential influenza outbreaks during COVID-19 pandemic, we studied the molecular epidemiology and phylogenetics of influenza A(H3N2) and B/Victoria that circulated during the COVID-19 pandemic in this region.
METHODS: From January 1, 2018 to December 31, 2022, we collected throat swabs from 173,401 patients in Guangdong who had acute respiratory tract infections. Influenza viruses in the samples were tested using reverse transcription-polymerase chain reaction, followed by subtype identification and sequencing of hemagglutinin (HA) and neuraminidase (NA) genes. Phylogenetic and genetic diversity analyses were performed on both genes from 403 samples. A rigorous molecular clock was aligned with the phylogenetic tree to measure the rate of viral evolution and the root-to-tip distance within strains in different years was assessed using regression curve models to determine the correlation.
RESULTS: During the early period of COVID-19 control, various influenza viruses were nearly undetectable in respiratory specimens. When control measures were relaxed in January 2020, the influenza infection rate peaked at 4.94% (39/789) in December 2021, with the influenza B/Victoria accounting for 87.18% (34/39) of the total influenza cases. Six months later, the influenza infection rate again increased and peaked at 11.34% (255/2248) in June 2022; influenza A/H3N2 accounted for 94.51% (241/255) of the total influenza cases in autumn 2022. The diverse geographic distribution of HA genes of B/Victoria and A/H3N2 had drastically reduced, and most strains originated from China. The rate of B/Victoria HA evolution (3.11 × 10-3, P < 0.05) was 1.7 times faster than before the COVID-19 outbreak (1.80 × 10-3, P < 0.05). Likewise, the H3N2 HA gene\'s evolution rate was 7.96 × 10-3 (P < 0.05), which is 2.1 times faster than the strains\' pre-COVID-19 evolution rate (3.81 × 10-3, P < 0.05).
CONCLUSIONS: Despite the extraordinarily low detection rate of influenza infection, concealed influenza transmission may occur between individuals during strict COVID-19 control. This ultimately leads to the accumulation of viral mutations and accelerated evolution of H3N2 and B/Victoria viruses. Monitoring the evolution of influenza may provide insights and alerts regarding potential epidemics in the future.

The aim of this study was to determine the level of anti-hemagglutinin antibodies in blood sera collected from patients during the 2022/2023 epidemic season in Poland. A total of 700 sera samples from patients across the country were tested. The samples were divided into seven groups according to the age of the patients, with 100 samples from each age group. The hemagglutination inhibition test (OZHA) was used to determine the level of anti-hemagglutinin antibodies. The test results have confirmed the presence of anti-hemagglutinin antibodies for antigens A/Victoria/2570/2019 (H1N1)pdm09, A/Darwin/9/2021 (H3N2), B/Austria/1359417/2021 (B/Yamagata lineage) and B/ Phuket/3073/2013 (B/Victoria lineage) present in the influenza vaccine recommended by the World Health Organization (WHO) for the 2022/2023 epidemic season. The highest geometric mean antibody titres (GMT) and protection rate values (%) were recorded for hemagglutinin A/H3N2. In Poland, in the 2022/2023 epidemic season, the percentage of the population vaccinated against influenza was 5.7%. Therefore, the test results can be interpreted as the response of the immune system in patients who have been previously infected with an influenza virus.

Influenza A/H9 viruses circulate worldwide in wild and domestic avian species, continuing to evolve and posing a zoonotic risk. A substantial increase in human infections with A/H9N2 subtype avian influenza viruses (AIVs) and the emergence of novel reassortants carrying A/H9N2-origin internal genes has occurred in recent years. Different names have been used to describe the circulating and emerging A/H9 lineages. To address this issue, an international group of experts from animal and public health laboratories, endorsed by the WOAH/FAO Network of Expertise on Animal Influenza, has created a practical lineage classification and nomenclature system based on the analysis of 10,638 hemagglutinin sequences from A/H9 AIVs sampled worldwide. This system incorporates phylogenetic relationships and epidemiologic characteristics designed to trace emerging and circulating lineages and clades. To aid in lineage and clade assignment, an online tool has been created. This proposed classification enables rapid comprehension of the global spread and evolution of A/H9 AIVs.

Influenza A virus (IAV) is well known for its pandemic potential. While current surveillance and vaccination strategies are highly effective, therapeutic approaches are often short-lived due to the high mutation rates of IAV. Recently, monoclonal antibodies (mAbs) have emerged as a promising therapeutic approach, both against current strains and future IAV pandemics. In addition to mAbs, several antibody-like alternatives exist, which aim to improve upon mAbs. Among these, Affimers stand out for their short development time, high expression levels in Escherichia coli, and animal-free production. In this study, we utilized the Affimer platform to isolate and produce specific and potent inhibitors of IAV. Using a monomeric version of the IAV trimeric hemagglutinin (HA) fusion protein, we isolated 12 Affimers that inhibit IAV infection in vitro. Two of these Affimers were characterized in detail and exhibited nanomolar-binding affinities to the target H3 HA protein, specifically binding to the HA1 head domain. Cryo-electron microscopy (cryo-EM), employing a novel spray approach to prepare cryo-grids, allowed us to image HA-Affimer complexes. Combined with functional assays, we determined that these Affimers inhibit IAV by blocking the interaction of HA with the host-cell receptor, sialic acid. Furthermore, these Affimers inhibited IAV strains closely related to the one used for their isolation. Overall, our results support the use of Affimers as a viable alternative to existing targeted therapies for IAV and highlight their potential as diagnostic reagents.
OBJECTIVE: Influenza A virus is one of the few viruses that can cause devastating pandemics. Due to the high mutation rates of this virus, annual vaccination is required, and antivirals are short-lived. Monoclonal antibodies present a promising approach to tackle influenza virus infections but are associated with some limitations. To improve on this strategy, we explored the Affimer platform, which are antibody-like proteins made in bacteria. By performing phage-display against a monomeric version of influenza virus fusion protein, an established viral target, we were able to isolate Affimers that inhibit influenza virus infection in vitro. We characterized the mechanism of inhibition of the Affimers by using assays targeting different stages of the viral replication cycle. We additionally characterized HA-Affimer complex structure, using a novel approach to prepare samples for cryo-electron microscopy. Overall, these results show that Affimers are a promising tool against influenza virus infection.

Human influenza virus evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the antigenic effects of viral mutations vary across the human population and how this heterogeneity affects virus evolution. Here, we use deep mutational scanning to map how mutations to the hemagglutinin (HA) proteins of two H3N2 strains, A/Hong Kong/45/2019 and A/Perth/16/2009, affect neutralization by serum from individuals of a variety of ages. The effects of HA mutations on serum neutralization differ across age groups in ways that can be partially rationalized in terms of exposure histories. Mutations that were fixed in influenza variants after 2020 cause greater escape from sera from younger individuals compared with adults. Overall, these results demonstrate that influenza faces distinct antigenic selection regimes from different age groups and suggest approaches to understand how this heterogeneous selection shapes viral evolution.