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Genomic features associated with sustained mammalian transmission of avian influenza A viruses

Nature Microbiology volume 11, pages 802–814 (2026)Cite this article

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Abstract

Comparably few lineages of influenza A virus (IAV) have evolved long-term sustained transmission in mammals. The reasons remain largely unknown, and the possibility of avian IAVs evolving sustained mammalian transmission is an ongoing concern. Here we measured the GC content and frequency of GC dinucleotides in 115,520 whole genomes of IAVs using bioinformatic analyses. We found that persistent mammalian lineages showed declining trends in GC-related content and could be reliably separated from IAVs circulating only in birds and those sporadically infecting mammals. Similarly, the earliest viruses of persistent mammalian lineages showed reduced GC-related content, suggesting that this trait might in part contribute to their eventual persistence. Recent highly pathogenic 2.3.4.4b H5 viruses that spread in mink, foxes and humans were also characterized by reduced GC-related content. While not sufficient, reduced GC-related content may be a necessary condition for sustained mammalian transmission and should be included in risk assessment tools for pandemic influenza.

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Fig. 1: Assignment of host-specific lineages for IAVs.
Fig. 2: Longitudinal evolution of GC content in persistent mammalian lineages.
Fig. 3: Association between GC dinucleotide frequency and GC content.
Fig. 4: Classification of persistent mammalian lineages and avian and sporadic mammalian IAVs.
Fig. 5: Prediction of the capacity for sustained mammalian transmission of the earliest sequences in persistent mammalian lineages and recently emerging IAVs.
Fig. 6: Epidemiology of the G1 genotype of H5N1 clade 2.3.4.4b.

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Data availability

The IAV genomic data used in this study are available in GISAID with accession numbers provided in EPI_SET_20220531ye and EPI_SET_250116bq at https://www.gisaid.org/. The phylogenetic trees of the eight protein coding regions used to classify persistent mammalian lineages and of the more recent H5Nx genomes, the summary of statistics for genomic GC content, GC dinucleotide frequencies, codon adaption index and free energy of RNA folding, and SVM model files are publicly accessible via GitHub at https://github.com/id-bioinfo/IAV_GCContent.

Code availability

The SVM model is available at https://iav-transmission.org/. Scripts for the analysis are accessible via GitHub at https://github.com/id-bioinfo/IAV_GCContent. Tools, packages and software used in this study are publicly available.

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Acknowledgements

This project is supported by the National Natural Science Foundation of China’s Excellent Young Scientists Fund (Hong Kong and Macau) (31922087), the Hong Kong Research Grants Council’s General Research Fund (17150816), the Health and Medical Research Fund (COVID1903011-WP1), the Innovation and Technology Commission’s InnoHK funding (D24H), the Theme Based Research Scheme (T11-705/21-N), the Hong Kong Jockey Club Global Health Institute and the Guangdong Government for the funding support (2019B121205009, HZQB-KCZYZ-2021014, 200109155890863, 190830095586328 and 190824215544727). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the paper. We gratefully acknowledge the authors from the laboratories responsible for obtaining the specimens and those who generated and shared the sequence data to GISAID. The acknowledgement tables for IAVs can be found with EPI_SET_20220531ye and EPI_SET_250116bq. We thank S. Lycett and W. Harvey for sharing the genotype assignment of H5 IAVs in Europe.

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Authors and Affiliations

  1. State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Hong Kong SAR, People’s Republic of China

    Yongtao Ye, Yi Song, Marcus Ho-Hin Shum, David K. Smith, Huachen Zhu, Yi Guan & Tommy Tsan-Yuk Lam

  2. School of Public Health, The University of Hong Kong, Hong Kong SAR, People’s Republic of China

    Yongtao Ye, Yi Song, Marcus Ho-Hin Shum, David K. Smith, Haogao Gu, Huachen Zhu, Joseph T. Wu, Malik Peiris, Leo Lit-Man Poon, Yi Guan & Tommy Tsan-Yuk Lam

  3. Laboratory of Data Discovery for Health, Hong Kong SAR, People’s Republic of China

    Yongtao Ye, Yi Song, Marcus Ho-Hin Shum, David K. Smith, Huachen Zhu, Joseph T. Wu, Edward C. Holmes, Yi Guan & Tommy Tsan-Yuk Lam

  4. Department of Microbiology, The University of Hong Kong, Hong Kong SAR, People’s Republic of China

    Huiping Shuai

  5. Guangdong–Hongkong Joint Laboratory of Emerging Infectious Diseases, Joint Institute of Virology (Shantou University/The University of Hong Kong), Shantou, People’s Republic of China

    Huachen Zhu, Yi Guan & Tommy Tsan-Yuk Lam

  6. The Hong Kong Jockey Club Global Health Institute, Hong Kong SAR, People’s Republic of China

    Joseph T. Wu, Malik Peiris, Leo Lit-Man Poon & Tommy Tsan-Yuk Lam

  7. Worldwide Influenza Centre, The Francis Crick Institute, London, UK

    Nicola S. Lewis

  8. Department of Pathobiology and Population Sciences, The Royal Veterinary College, London, UK

    Nicola S. Lewis

  9. Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Italy

    Francesco Bonfante

  10. School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Edward C. Holmes

  11. Centre for Immunology & Infection, Hong Kong SAR, People’s Republic of China

    Malik Peiris, Leo Lit-Man Poon & Tommy Tsan-Yuk Lam

  12. HKU-Pasteur Research Pole, Hong Kong SAR, People’s Republic of China

    Malik Peiris, Leo Lit-Man Poon & Tommy Tsan-Yuk Lam

  13. Shanghai Institute of Virology, Shanghai, People’s Republic of China

    Yi Guan

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Contributions

Y.Y., Y.G. and T.T.-Y.L. conceived the study. Y.Y. conducted data collection and analyses. Y.S. performed the phylogeographic analysis. T.T.-Y.L. supervised the study. Y.Y., D.K.S. and E.C.H. developed the first draft of the paper. Y.Y., H.S., Y.S., M.H.-H.S., D.K.S., H.G., H.Z., J.T.W., N.S.L., F.B., E.C.H., M.P., L.L.-M.P., Y.G. and T.T.-Y.L. critically reviewed the analysis results, revised the paper and approved it for submission.

Corresponding authors

Correspondence to Yi Guan or Tommy Tsan-Yuk Lam.

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Extended data

Extended Data Fig. 1 Longitudinal evolution of codon usage bias in persistent mammalian lineages.

Scatter plots show the evolution of codon adaptation index (CAI) of the combined eight CDSs (PB2, PB1, PA, HA, NP, NA, M1 and NS1) for the 12 persistent mammalian IAV lineages over sample collection time.

Extended Data Fig. 2 Correlation coefficient between the free energy of RNA folding and GC/GC3/GC12 content.

Heat map shows the Pearson correlation coefficients between minimum free energy (MFE) of RNA secondary structure and GC content at all (GC), the third (GC3), and first and second (GC12) codon sites in the eight CDSs for 12 persistent mammalian lineages. A cross indicates a correlation is not statistically significant (p-value >= 0.05). Blue color shows negative correlation, as red for positive.

Extended Data Fig. 3 Genomic GC content of avian IAVs.

Avian IAVs are shown by host families with more than 5 sequences that are sorted by 25th percentile in descending order from left to right. Genomic GC content of the 12 persistent mammalian lineages is present as reference. The whiskers represent the minimum and maximum values while the box shows the lower and upper quartiles with the median crossing the box.

Extended Data Fig. 4 Distribution of the combined avian and sporadic mammalian IAVs and the persistent mammalian lineages based on features from individual CDS.

LDA (linear discriminant analysis) projection using features from each individual CDS. The linear discriminant coordinates 1 (LD1) and 2 (LD2) are the x-y axes. Explained variations are given in parentheses. Distributions of the combined avian and sporadic mammalian IAVs (gray regions), and the persistent mammalian lineages (red regions) were computed using kernel density estimation. Positions of the earliest sequences in the 12 persistent mammalian lineages are present where Hu1 (red), Sw2 (blue), Eq (green) and Ca2 (yellow) wholly derived from avian IAVs are highlighted while others are colored in light blue.

Extended Data Fig. 5 Correlation of receptor binding, polymerase activity and viral transmissibility against genomic GC content.

Linear regression of genomic GC content over a, viral receptor-binding preference to α-2,3-linked sialosides and α-2,6-linked sialosides (n = 6), b, viral polymerase activity in NPTr and 293 T cells (n = 3), c, viral loads in the donor nasal swabs and onward transmissibility in seroconversion (n = 4). R2, p-value and 95% confidence intervals are shown around the regression line. Each data point represents the mean value of the following tested viruses: DK/77, Sw2/81, Sw2/92, RG-EA1, RG-EA2, RG-EA3 and RG-EA4, and is arranged by genomic GC content. Source data were derived from Su W. et al., 2021. SIA, sialoside; r.f.u., relative fluorescence units.

Extended Data Fig. 6 Proportion of complete genome sequences of clade 2.3.4.4b viruses with molecular markers.

Heatmap of selected molecular markers (protein mutations) associated with mammalian adaptation by (a) genotype and (b) those found in mammalian IAVs of genotype G1 or not G1. Mutations unique to the G1 genotype are highlighted by red boxes. Two sub-genotypes of B3.2 and B3.13 are shown for pinnipeds and cattle H5 viruses respectively.

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Ye, Y., Shuai, H., Song, Y. et al. Genomic features associated with sustained mammalian transmission of avian influenza A viruses. Nat Microbiol 11, 802–814 (2026). https://doi.org/10.1038/s41564-025-02257-4

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