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Individual immune selection pressure has limited impact on seasonal influenza virus evolution

Abstract

Seasonal influenza viruses are subjected to strong selection as seen by the sequential replacement of existing viral populations on the emergence of new antigenic variants. However, the process of within-host de novo mutant generation and evolutionary selection that underlies these antigenic sweeps is poorly understood. Here, we investigate mutational patterns between evolutionarily closely related human seasonal influenza viruses using host age as a proxy for immune experience. The systematic analysis of >25,000 virus sequences showed that individuals with substantially differing immune histories were frequently (30–62%) infected by viruses with identical amino acid sequences. Viruses from immunologically inexperienced individuals were as likely to possess substitutions with potential phenotypic relevance as highly experienced individuals. Mutations likely to cause antigenic changes were rare among closely related viruses and not associated with extent of host immune experience. These findings suggest that individual immune positive selection plays a limited role in the evolution of seasonal influenza viruses.

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Fig. 1: Maximum-likelihood phylogenetic tree of A/H1N1pdm09 neuraminidase (2011–2014).
Fig. 2: Association analyses for detecting significant differences in amino acid changes between end-in-adult and end-in-child pairs using different combinations of minimum adult and maximum child thresholds.
Fig. 3: Distribution of virus sequence pairs stratified by number of amino acid substitutions observed and pair types.
Fig. 4: Haemagglutinin crystal structures of seasonal influenza viruses.

Data availability

Processed data, including curated sequence alignments, metadata and phylogenetic trees generated, are available on Github: http://github.com/alvinxhan/ageflu.

References

  1. Petrova, V. N. & Russell, C. A. The evolution of seasonal influenza viruses. Nat. Rev. Microbiol. 16, 47–60 (2017).

    PubMed  Article  CAS  Google Scholar 

  2. Koel, B. F. et al. Substitutions near the receptor binding site determine major antigenic change during influenza virus evolution. Science 342, 976–979 (2013).

    CAS  PubMed  Article  Google Scholar 

  3. Smith, D. J. et al. Mapping the antigenic and genetic evolution of influenza virus. Science 305, 371–376 (2004).

    CAS  PubMed  Article  Google Scholar 

  4. Hensley, S. E. et al. Hemagglutinin receptor binding avidity drives influenza A virus antigenic drift. Science 326, 734–736 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Cao, P. et al. Age-specific genetic and antigenic variations of influenza A viruses in Hong Kong, 2013–2014. Sci. Rep. 6, 30260 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Dinis, J. M. et al. Deep sequencing reveals potential antigenic variants at low frequencies in influenza A virus-infected humans. J. Virol. 90, 3355–3365 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Sobel Leonard, A. et al. Deep sequencing of influenza A virus from a human challenge study reveals a selective bottleneck and only limited intrahost genetic diversification. J. Virol. 90, 11247–11258 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  8. Debbink, K. et al. Vaccination has minimal impact on the intrahost diversity of H3N2 influenza viruses. PLoS Pathog. 13, e1006194 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. McCrone, J. T. et al. Stochastic processes constrain the within and between host evolution of influenza virus. eLife 7, e35962 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  10. Davis, A. K. F. et al. Sera from Individuals with narrowly focused influenza virus antibodies rapidly select viral escape mutations in ovo. J. Virol. 92, e00859-18 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  11. Su, Y. C. F. et al. Phylodynamics of H1N1/2009 influenza reveals the transition from host adaptation to immune-driven selection. Nat. Commun. 6, 7952 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Bodewes, R. et al. Prevalence of antibodies against seasonal influenza A and B viruses in children in the Netherlands. Clin. Vaccine Immunol. 18, 469–476 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Sauerbrei, A. et al. Prevalence of antibodies against influenza A and B viruses in children in Germany, 2008 to 2010. Eurosurveillance 19, 20687 (2014).

    PubMed  Article  Google Scholar 

  14. Kucharski, A. J. et al. Estimating the life course of influenza A(H3N2) antibody responses from cross-sectional data. PLoS Biol. 13, e1002082 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. Mai, L. Q. et al. A community cluster of oseltamivir-resistant cases of 2009 H1N1 influenza. N. Engl. J. Med. 362, 86–87 (2010).

    CAS  Article  Google Scholar 

  16. Storms, A. D. et al. Oseltamivir-resistant pandemic (H1N1) 2009 virus infections, United States, 2010–11. Emerg. Infect. Dis.J. 18, 308–311 (2012).

    Article  Google Scholar 

  17. Hurt, A. C. et al. Characteristics of a widespread community cluster of H275Y oseltamivir-resistant A(H1N1)pdm09 influenza in Australia. J. Infect. Dis. 206, 148–157 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Huang, W. et al. Characteristics of oseltamivir-resistant influenza A (H1N1) pdm09 virus during the 2013-2014 influenza season in mainland China. Virol. J. 12, 96 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. Neher, R. A., Bedford, T., Daniels, R. S., Russell, C. A. & Shraiman, B. I. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. Proc. Natl Acad. Sci. USA 113, E1701–E1709 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. Ohuchi, M., Ohuchi, R., Feldmann, A. & Klenk, H. D. Regulation of receptor binding affinity of influenza virus hemagglutinin by its carbohydrate moiety. J. Virol. 71, 8377–8384 (1997).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Alymova, I. V. et al. Glycosylation changes in the globular head of H3N2 influenza hemagglutinin modulate receptor binding without affecting virus virulence. Sci. Rep. 6, 36216 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Tate, D. M. et al. Playing hide and seek: how glycosylation of the influenza virus hemagglutinin can modulate the immune response to infection. Viruses 6, 1294–1316 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. Del Giudice, G. et al. Fighting against a protean enemy: immunosenescence, vaccines, and healthy aging. NPJ Aging Mech. Dis. 4, 1 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  24. Chen, H. et al. Dynamic convergent evolution drives the passage adaptation across 48 years’ history of H3N2 influenza evolution. Mol. Biol. Evol. 33, 3133–3143 (2016).

    CAS  PubMed  Article  Google Scholar 

  25. McWhite, C. D., Meyer, A. G. & Wilke, C. O. Sequence amplification via cell passaging creates spurious signals of positive adaptation in influenza virus H3N2 hemagglutinin. Virus Evol. 2, vew026 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  26. Vijaykrishna, D. et al. The contrasting phylodynamics of human influenza B viruses. eLife 4, e05055 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  27. Takemae, N. et al. Alterations in receptor-binding properties of swine influenza viruses of the H1 subtype after isolation in embryonated chicken eggs. J. Gen. Virol. 91, 938–948 (2010).

    CAS  PubMed  Article  Google Scholar 

  28. Nicholls, J. M., Bourne, A. J., Chen, H., Guan, Y. & Peiris, J. S. M. Sialic acid receptor detection in the human respiratory tract: evidence for widespread distribution of potential binding sites for human and avian influenza viruses. Respir. Res. 8, 73 (2007).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  29. Walther, T. et al. Glycomic analysis of human respiratory tract tissues and correlation with influenza virus infection. PLoS Pathog. 9, e1003223 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Chutinimitkul, S. et al. Virulence-associated substitution D222G in the hemagglutinin of 2009 pandemic influenza A(H1N1) virus affects receptor binding. J. Virol. 84, 11802–11813 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Liu, Y. et al. Altered receptor specificity and cell tropism of D222G hemagglutinin mutants isolated from fatal cases of pandemic A(H1N1) 2009 influenza virus. J. Virol. 84, 12069–12074 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  32. Zhang, W. et al. Molecular basis of the receptor binding specificity switch of the hemagglutinins from both the 1918 and 2009 pandemic influenza A viruses by a D225G substitution. J. Virol. 87, 5949–5958 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Chen, H. et al. Quasispecies of the D225G substitution in the hemagglutinin of pandemic influenza A(H1N1) 2009 virus from patients with severe disease in Hong Kong, China. J. Infect. Dis. 201, 1517–1521 (2010).

    CAS  PubMed  Article  Google Scholar 

  34. Puzelli, S. et al. Transmission of hemagglutinin D222G mutant strain of pandemic (H1N1) 2009 virus. Emerg. Infect. Dis.J. 16, 863–865 (2010).

    Article  Google Scholar 

  35. Kilander, A., Rykkvin, R., Dudman, S. G. & Hungnes, O. Observed association between the HA1 mutation D222G in the 2009 pandemic influenza A(H1N1) virus and severe clinical outcome, Norway 2009-2010. Eurosurveillance 15, 19498 (2010).

    PubMed  Google Scholar 

  36. Mak, G. C. et al. Association of D222G substitution in haemagglutinin of 2009 pandemic influenza A (H1N1) with severe disease. Eurosurveillance 15, 19534 (2010).

    PubMed  Article  Google Scholar 

  37. Iovine, N. M. et al. Severity of influenza A(H1N1) illness and emergence of D225G variant, 2013-14 influenza season, Florida, USA. Emerg. Infect. Dis. J. 21, 664–667 (2015).

    CAS  Article  Google Scholar 

  38. Shinya, K. et al. Influenza virus receptors in the human airway. Nature 440, 435–436 (2006).

    CAS  PubMed  Article  Google Scholar 

  39. Xue, K. S. et al. Parallel evolution of influenza across multiple spatiotemporal scales. eLife 6, e26875 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  40. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M. & Barton, G. J. Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–1191 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Zwickl, D. J. Genetic Algorithm Approaches for the Phylogenetic Analysis of Large Biological Sequence Datasets under the Maximum Likelihood Criterion. PhD thesis, Univ. Texas at Austin (2006).

  46. Fitch, W. M., Bush, R. M., Bender, C. A. & Cox, N. J. Long term trends in the evolution of H(3) HA1 humaninfluenza type A.Proc. Natl Acad. Sci. USA 94, 7712–7718 (1997).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. Yang, H. et al. Structure and receptor binding preferences of recombinant human A(H3N2) virus hemagglutinins. Virology 477, 18–31 (2015).

    CAS  PubMed  Article  Google Scholar 

  48. Wang, Q., Tian, X., Chen, X. & Ma, J. Structural basis for receptor specificity of influenza B virus hemagglutinin. Proc. Natl Acad. Sci. USA 104, 16874–16879 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. Stray, S. J. & Pittman, L. B. Subtype- and antigenic site-specific differences in biophysical influences on evolution of influenza virus hemagglutinin. Virol. J. 9, 91 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2016).

  51. Plummer, M. rjags: Bayesian Graphical Models using MCMC (Sourceforge, 2016).

  52. Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence Diagnosis and Output Analysis for MCMC. R News 6, 7–11 (2006).

    Google Scholar 

  53. Kateri, M. Contingency Table Analysis (Springer, New York, 2014).

  54. Barnard, G. A. A new test for 2×2 tables. Nature 156, 177 (1945).

    Article  Google Scholar 

  55. Signorell, A. et al. DescTools: Tools for Descriptive Statistics R Package Version 0.99.26 (2018).

  56. Erguler, K. Barnard: Barnard’s Unconditional Test (CRAN, 2016).

  57. Hoenig, J. M. & Heisey, D. M. The abuse of power. Am. Stat. 55, 19–24 (2001).

    Article  Google Scholar 

  58. Colegrave, N. & Ruxton, G. D. Confidence intervals are a more useful complement to nonsignificant tests than are power calculations. Behav. Ecol. 14, 446–447 (2003).

    Article  Google Scholar 

  59. Walker, E. & Nowacki, A. S. Understanding equivalence and noninferiority testing. J. Gen. Intern. Med. 26, 192–196 (2011).

    PubMed  Article  Google Scholar 

  60. Ekiert, D. C. et al. Cross-neutralization of influenza A viruses mediated by a single antibody loop. Nature 489, 526–532 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Dreyfus, C. et al. Highly conserved protective epitopes on influenza B viruses. Science 337, 1343–1348 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382–W388 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Goncearenco, A. et al. SPACER: server for predicting allosteric communication and effects of regulation. Nucleic Acids Res. 41, W266–W272 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

We thank the GISAID Initiative and the influenza surveillance and research groups that openly shared the genetic sequence data that made this work possible. A.X.H. was supported by the A*STAR Graduate Scholarship programme from A*STAR to carry out his PhD work via collaboration between Bioinformatics Institute (A*STAR) and NUS Graduate School for Integrative Sciences and Engineering from the National University of Singapore. S.M.S. was supported by the A*STAR HEIDI programme (grant number H1699f0013) and Bioinformatics Institute (A*STAR). C.A.R. was supported by University Research Fellowship from the Royal Society.

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C.A.R. conceived the project. All authors designed the experiments. A.X.H. performed the analyses. All authors analysed the results. A.X.H. and C.A.R. wrote and revised the manuscript with substantial input from S.M.S.

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Correspondence to Colin A. Russell.

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Han, A.X., Maurer-Stroh, S. & Russell, C.A. Individual immune selection pressure has limited impact on seasonal influenza virus evolution. Nat Ecol Evol 3, 302–311 (2019). https://doi.org/10.1038/s41559-018-0741-x

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