Human Immunity and Susceptibility to Influenza A(H3) Viruses of Avian, Equine, and Swine Origin

Early-warning signals and the role of H9N2 in the spillover of avian influenza viruses

BACKGROUND

The spillover of avian influenza viruses (AIVs) presents a significant global public health threat, leading to unpredictable and recurring pandemics. Current pandemic assessment tools suffer from deficiencies in terms of timeliness, capability for automation, and ability to generate risk estimates for multiple subtypes in the absence of documented human cases.

METHODS

To address these challenges, we created an integrated database encompassing global AIV-related data from 1981 to 2022. This database enabled us to estimate the rapid expansion of spatial range and host diversity for specific AIV subtypes, alongside their increasing prevalence in hosts that have close contact with humans. These factors were used as early-warning signals for potential AIV spillover. We analyzed spillover patterns of AIVs using machine learning models, spatial Durbin models, and phylogenetic analysis.

FINDINGS

Our results indicate a high potential for future spillover by subtypes H3N1, H4N6, H5N2, H5N3, H6N2, and H11N9. Additionally, we identified a significant risk for re-emergence by subtypes H5N1, H5N6, H5N8, and H9N2. Furthermore, our analysis highlighted 12 key strains of H9N2 as internal genetic donors for human adaptation in AIVs, demonstrating the crucial role of H9N2 in facilitating AIV spillover.

CONCLUSIONS

These findings provide a foundation for rapidly identifying high-risk subtypes, thus optimizing resource allocation in vaccine manufacture. They also underscore the potential significance of reducing the prevalence of H9N2 as a complementary strategy to mitigate chances of AIV spillovers.

FUNDING

National Key Research and Development Program of China.

Long-term co-circulation of multiple influenza A viruses in pigs, Guangxi, China

Influenza A viruses (IAVs) pose a persistent potential threat to human health because of the spillover from avian and swine infections. Extensive surveillance was performed in 12 cities of Guangxi, China, during 2018 and 2023. A total of 2540 samples (including 2353 nasal swabs and 187 lung tissues) were collected from 18 pig farms with outbreaks of respiratory disease. From these, 192 IAV-positive samples and 19 genomic sequences were obtained. We found that the H1 and H3 swine influenza A viruses (swIAVs) of multiple lineages and genotypes have continued to co-circulate during that time in this region. Genomic analysis revealed the Eurasian avian-like H1N1 swIAVs (G4) still remained predominant in pig populations. Strikingly, the novel multiple H3N2 genotypes were found to have been generated through the repeated introduction of the early H3N2 North American triple reassortant viruses (TR H3N2 lineage) that emerged in USA and Canada in 1998 and 2005, respectively. Notably, when the matrix gene segment derived from the H9N2 avian influenza virus was introduced into endemic swIAVs, this produced a novel quadruple reassortant H1N2 swIAV that could pose a potential risk for zoonotic infection.

Utilizing machine learning and hemagglutinin sequences to identify likely hosts of influenza H3Nx viruses

Influenza is a disease that represents both a public health and agricultural risk with pandemic potential. Among the subtypes of influenza A virus, H3 influenza virus can infect many avian and mammalian species and is therefore a virus of interest to human and veterinary public health. The primary goal of this study was to train and validate classifiers for the identification of the most likely host species using the hemagglutinin gene segment of H3 viruses. A five-step process was implemented, which included training four machine learning classifiers, testing the classifiers on the validation dataset, and further exploration of the best-performing model on three additional datasets. The gradient boosting machine classifier showed the highest host-classification accuracy with a 98.0 % (95 % CI [97.01, 98.73]) correct classification rate on an independent validation dataset. The classifications were further analyzed using the predicted probability score which highlighted sequences of particular interest. These sequences were both correctly and incorrectly classified sequences that showed considerable predicted probability for multiple hosts. This showed the potential of using these classifiers for rapid sequence classification and highlighting sequences of interest. Additionally, the classifiers were tested on a separate swine dataset composed of H3N2 sequences from 1998 to 2003 from the United States of America, and a separate canine dataset composed of canine H3N2 sequences of avian origin. These two datasets were utilized to look at the applications of predicted probability and host convergence over time. Lastly, the classifiers were used on an independent dataset of environmental sequences to explore the host identification of environmental sequences. The results of these classifiers show the potential for machine learning to be used as a host identification technique for viruses of unknown origin on a species-specific level.

First Known Human Death After Infection With the Avian Influenza A/H3N8 Virus: Guangdong Province, China, March 2023

Here, we report on a case of human infection with the H3N8 avian influenza virus. The patient had multiple myeloma and died of severe infection. Genome analysis showed multiple gene mutations and reassortments without mammalian-adaptive mutations. This suggests that avian influenza (A/H3N8) virus infection could be lethal for immunocompromised persons.

Looking beyond the H5 avian influenza viruses

Avian influenza viruses continue to cross the species barrier and infect mammals. In this issue of Cell, Sun and colleagues demonstrate that viruses obtained from humans infected with an emergent avian H3N8 viruses exhibit increasing accumulation of mutations that promote respiratory droplet transmission and disease in mammals.