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The genomic and epidemiological dynamics of human influenza A virus

Author

Listed:
  • Andrew Rambaut

    (Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories)

  • Oliver G. Pybus

    (University of Oxford, South Parks Road, Oxford OX1 3PS, UK)

  • Martha I. Nelson

    (Center for Infectious Disease Dynamics, The Pennsylvania State University, Mueller Laboratory, University Park, Pennsylvania 16802, USA)

  • Cecile Viboud

    (Fogarty International Center, National Institutes of Health, Bethesda, Maryland 20892, USA)

  • Jeffery K. Taubenberger

    (Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA)

  • Edward C. Holmes

    (Center for Infectious Disease Dynamics, The Pennsylvania State University, Mueller Laboratory, University Park, Pennsylvania 16802, USA
    Fogarty International Center, National Institutes of Health, Bethesda, Maryland 20892, USA)

Abstract

The evolutionary interaction between influenza A virus and the human immune system, manifest as ‘antigenic drift’ of the viral haemagglutinin, is one of the best described patterns in molecular evolution. However, little is known about the genome-scale evolutionary dynamics of this pathogen. Similarly, how genomic processes relate to global influenza epidemiology, in which the A/H3N2 and A/H1N1 subtypes co-circulate, is poorly understood. Here through an analysis of 1,302 complete viral genomes sampled from temperate populations in both hemispheres, we show that the genomic evolution of influenza A virus is characterized by a complex interplay between frequent reassortment and periodic selective sweeps. The A/H3N2 and A/H1N1 subtypes exhibit different evolutionary dynamics, with diverse lineages circulating in A/H1N1, indicative of weaker antigenic drift. These results suggest a sink–source model of viral ecology in which new lineages are seeded from a persistent influenza reservoir, which we hypothesize to be located in the tropics, to sink populations in temperate regions.

Suggested Citation

  • Andrew Rambaut & Oliver G. Pybus & Martha I. Nelson & Cecile Viboud & Jeffery K. Taubenberger & Edward C. Holmes, 2008. "The genomic and epidemiological dynamics of human influenza A virus," Nature, Nature, vol. 453(7195), pages 615-619, May.
    • Handle: RePEc:nat:nature:v:453:y:2008:i:7195:d:10.1038_nature06945
    DOI: 10.1038/nature06945
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    Cited by:

    1. Katherine A. Amato & Luis A. Haddock & Katarina M. Braun & Victoria Meliopoulos & Brandi Livingston & Rebekah Honce & Grace A. Schaack & Emma Boehm & Christina A. Higgins & Gabrielle L. Barry & Katia , 2022. "Influenza A virus undergoes compartmentalized replication in vivo dominated by stochastic bottlenecks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Maud Thomas & Holger Rootzén, 2022. "Real‐time prediction of severe influenza epidemics using extreme value statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 376-394, March.
    3. Simin Zou & Xuhui He, 2021. "Effect of Train-Induced Wind on the Transmission of COVID-19: A New Insight into Potential Infectious Risks," IJERPH, MDPI, vol. 18(15), pages 1-17, August.
    4. Vijaykrishna Dhanasekaran & Sheena Sullivan & Kimberly M. Edwards & Ruopeng Xie & Arseniy Khvorov & Sophie A. Valkenburg & Benjamin J. Cowling & Ian G. Barr, 2022. "Human seasonal influenza under COVID-19 and the potential consequences of influenza lineage elimination," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. Kin Keung Lai & Ming Wang & Jiangze Du, 2019. "Modeling and Predicting Infectious Diseases Cases with Climatic Factors in Hong Kong," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 23(1), pages 17147-17150, November.
    6. Kucharski, Adam J. & Gog, Julia R., 2012. "Age profile of immunity to influenza: Effect of original antigenic sin," Theoretical Population Biology, Elsevier, vol. 81(2), pages 102-112.
    7. Good, Benjamin H. & Desai, Michael M., 2013. "Fluctuations in fitness distributions and the effects of weak linked selection on sequence evolution," Theoretical Population Biology, Elsevier, vol. 85(C), pages 86-102.
    8. Michael D Karcher & Julia A Palacios & Trevor Bedford & Marc A Suchard & Vladimir N Minin, 2016. "Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-19, March.
    9. Xi-Ling Wang & Lin Yang & King-Pan Chan & Susan S Chiu & Kwok-Hung Chan & J S Malik Peiris & Chit-Ming Wong, 2012. "Model Selection in Time Series Studies of Influenza-Associated Mortality," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-7, June.
    10. David A Rasmussen & Oliver Ratmann & Katia Koelle, 2011. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-11, August.
    11. Igor Balaz & Taichi Haruna, 2018. "Evolution Of Influenza A Nucleotide Segments Through The Lens Of Different Complexity Measures," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1-24, August.
    12. Jingzhi Lou & Weiwen Liang & Lirong Cao & Inchi Hu & Shi Zhao & Zigui Chen & Renee Wan Yi Chan & Peter Pak Hang Cheung & Hong Zheng & Caiqi Liu & Qi Li & Marc Ka Chun Chong & Yexian Zhang & Eng-kiong , 2024. "Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    13. Joseph A. Lewnard & Vennis Hong & Jeniffer S. Kim & Sally F. Shaw & Bruno Lewin & Harpreet Takhar & Marc Lipsitch & Sara Y. Tartof, 2023. "Increased vaccine sensitivity of an emerging SARS-CoV-2 variant," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    14. James R. Faulkner & Andrew F. Magee & Beth Shapiro & Vladimir N. Minin, 2020. "Horseshoe‐based Bayesian nonparametric estimation of effective population size trajectories," Biometrics, The International Biometric Society, vol. 76(3), pages 677-690, September.

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