Low pathogenic avian influenza (LPAI) subtype H9N2 is a causative agent that has raised increasing concern about its impact on poultry and potential public health threats. Even though H9N2 is endemic in Peninsular Malaysia, it was first reported in Sabah in August 2022, after an outbreak associated with high mortality in broiler chickens. In the present study, based on the hemagglutinin (HA) gene, we report the genetic variations and phylogenetic analysis of a H9N2 virus isolated from broiler chickens in Sabah. The sequence analysis of the HA gene revealed a 98% similarity to the H9N2 virus recently isolated from China in 2018. The amino acids in the HA cleavage site displayed a characteristic LPAI motif (PARSSR/ GLF). Notably, at position 226, the isolate had amino acid Leucine (L) demonstrating its ability to bind to the receptor of mammals, resulting in the potential risk of transmission to humans. In addition, the H9N2 isolate harboured seven potential N-glycosylation sites. The phylogenetic analysis revealed that the isolate belonged to clade h9.4.2.5 in the Y280 lineage, similar to previously reported in Malaysia. However, we observed that the isolate in this study falls in a different cluster compared with previous Malaysian isolates, suggesting different source of H9N2 introduction into the country. This prompts us to propose continuous and thorough surveillance of poultry across the country and the necessity of implementing farm biosecurity to minimize economic losses and potential threats to public health.

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.

Influenza virus infection poses a continual menace to public health. Here, we developed soluble trimeric HA ectodomain vaccines by establishing interprotomer disulfide bonds in the stem region, which effectively preserve the native antigenicity of stem epitopes. The stable trimeric H1 ectodomain proteins exhibited higher thermal stabilities in comparison with unmodified HAs and showed strong binding activities towards a panel of anti-stem cross-reactive antibodies that recognize either interprotomer or intraprotomer epitopes. Negative stain transmission electron microscopy (TEM) analysis revealed the stable trimer architecture of the interprotomer disulfide-stapled WA11#5, NC99#2, and FLD#1 proteins as well as the irregular aggregation of unmodified HA molecules. Immunizations of mice with those trimeric HA ectodomain vaccines formulated with incomplete Freund\'s adjuvant elicited significantly more potent cross-neutralizing antibody responses and offered broader immuno-protection against lethal infections with heterologous influenza strains compared to unmodified HA proteins. Additionally, the findings of our study indicate that elevated levels of HA stem-specific antibody responses correlate with strengthened cross-protections. Our design strategy has proven effective in trimerizing HA ectodomains derived from both influenza A and B viruses, thereby providing a valuable reference for designing future influenza HA immunogens.

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.

Introduction. After two seasons of absence and low circulation, influenza activity increased significantly in the winter of 2022-2023. This study aims to characterize virological and epidemiological aspects of influenza infection in Bulgaria during the 2022-2023 season and perform a phylogenetic/molecular analysis of the hemagglutinin (HA) and neuraminidase (NA) sequences of representative influenza strains.Hypothesis/Gap Statement. Influenza A and B viruses generate new genetic groups/clades each season, replacing previously circulating variants. This results in increased antigenic distances from current vaccine strains. Strengthening existing influenza surveillance is essential to meet the challenges posed by the co-circulation of influenza and SARS-CoV-2.Methodology. We tested 2713 clinical samples from patients with acute respiratory illnesses using a multiplex real-time RT-PCR kit (FluSC2) to detect influenza A/B and Severe acute respiratory syndrome coronavirus-2(SARS-CoV-2) simultaneously. Representative Bulgarian influenza strains were sequenced at the WHO Collaborating Centres in London, UK, and Atlanta, USA.Results. Influenza virus was detected in 694 (25.6 %) patients. Of these, 364 (52.4 %), 213 (30.7 %) and 117 (16.9 %) were positive for influenza A(H1N1)pdm09, A(H3N2) and B/Victoria lineage virus, respectively. HA genes of the 47 influenza A(H1N1)pdm09 viruses fell into clades 5a.2. and 5a.2a.1 within the 6B.5A.1A.5a.2 group. Twenty-seven A(H3N2) viruses belonging to subclades 2b, 2a.1, 2a.1b and 2a.3a.1 within the 3C.2a1b.2a.2 group were analysed. All 23 sequenced B/Victoria lineage viruses were classified into the V1A.3a.2 group. We identified amino acid substitutions in HA and NA compared with the vaccine strains, including several substitutions in the HA antigenic sites.Conclusion. The study\'s findings showed genetic diversity among the influenza A viruses and, to a lesser extent, among B viruses, circulating in the first season after the lifting of anti-COVID-19 measures.

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.