Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype H3N2

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Abstract

Introduction. The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons.

The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data.

Materials and methods. In this study, a mathematical model was proposed, based on finding, using regression analysis, the dependence of HAI titers on substitutions in antigenic sites of sequences. The computer program we developed can process data (GISAID, NCBI, etc.) and create “real-time” databases according to the set tasks.

Results. Based on our research, an additional antigenic site F was identified. The difference in 1.6 times the adjusted R2, on subsets of viruses grown in cell culture and grown in chicken embryos, demonstrates the validity of our decision to divide the original data array by passage histories. We have introduced the concept of a degree of homology between two arbitrary strains, which takes the value of a function depending on the Hamming distance, and it has been shown that the regression results significantly depend on the choice of function. The provided analysis showed that the most significant antigenic sites are A, B, and E. The obtained results on predicted HAI titers showed a good enough result, comparable to similar work by our colleagues.

Conclusion. The proposed method could serve as a useful tool for future forecasts, with further study to confirm its sustainability.

About the authors

Marina N. Asatryan

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Author for correspondence.
Email: masatryan@gamaleya.org
ORCID iD: 0000-0001-6273-8615

PhD (Med.), senior researcher epidemiological cybernetics group of the Epidemiology Department

Russian Federation, 123098, Moscow

Boris I. Timofeev

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0000-0001-7425-0457

PhD (Phys.-Mat.), senior researcher D.I. Ivanovsky Institute of Virology Division

Russian Federation, 123098, Moscow

Ilya S. Shmyr

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-8514-5174

researcher epidemiological cybernetics group of the Epidemiology Department

Russian Federation, 123098, Moscow

Karlen R. Khachatryan

National Research University Higher School of Economics

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-1934-532X

master's student

Russian Federation, 123458, Moscow

Dmitrii N. Shcherbinin

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-8518-1669

PhD (Biol.), researcher, Department of Genetics and Molecular Biology of Bacteria

Russian Federation, 123098, Moscow

Tatiana A. Timofeeva

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-8991-8525

PhD (Biol.), head of laboratory D.I. Ivanovsky Institute of Virology Division

Russian Federation, 123098, Moscow

Elita R. Gerasimuk

State University “Dubna”

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-7364-163X

PhD (Med.), Assoc. Prof.

Russian Federation, 141982, Dubna

Vaagn G. Agasaryan

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0009-0009-3824-7061

researcher epidemiological cybernetics group of the Epidemiology Department

Russian Federation, 123098, Moscow

Ivan F. Ershov

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-3333-5347

researcher epidemiological cybernetics group of the Epidemiology Department

Russian Federation, 123098, Moscow

Tatyana I. Shashkova

Artificial Intelligence Research Institute

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-8754-8727

PhD (Biol.), senior researcher Bioinformatics group

Russian Federation, 121170, Moscow

Olga L. Kardymon

Artificial Intelligence Research Institute

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-4827-8891

head of Bioinformatics research group

Russian Federation, 121170, Moscow

Nikita V. Ivanisenko

Artificial Intelligence Research Institute

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-0333-8117

PhD (Biol.), senior researcher Bioinformatics group

Russian Federation, 121170, Moscow

Tatyana A. Semenenko

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0000-0002-6686-9011

D. Sci. (Med.), Prof., Full Member of RANS, head Department of Epidemiology

Russian Federation, 123098, Moscow

Boris S. Naroditsky

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0000-0001-5522-8238

D. Sci. (Biol.), professor, Deputy Director for research D.I. Ivanovsky Institute of Virology Division

Russian Federation, 123098, Moscow

Denis Yu. Logunov

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org

D. Sci. (Biol.), Full Member of RAS, Deputy Director for research

Russian Federation, 123098, Moscow

Aleksander L. Gintsburg

National Research Center for Epidemiology and Microbiology named after Honorary Academician N.F. Gamaleya

Email: masatryan@gamaleya.org
ORCID iD: 0000-0003-1769-5059

D. Sci. (Biol.), Prof., Full Member of RAS, Director

Russian Federation, 123098, Moscow

References

  1. Russell C.A., Jones T.C., Barr I.G., Cox N.J., Garten R.J., Gregory V., et al. Influenza vaccine strain selection and recent studies on the global migration of seasonal influenza viruses. Vaccine. 2008; 26(Suppl. 4): 31–4. https://doi.org/10.1016/j.vaccine.2008.07.078
  2. Belongia E.A., Simpson M.D., King J.P., Sundaram M.E., Kelley N.S., Osterholm M.T., et al. Variable influenza vaccine effectiveness by subtype: a systematic review and meta-analysis of test-negative design studies. Lancet Infect. Dis. 2016; 16(8): 942–51. https://doi.org/10.1016/S1473-3099(16)00129-8
  3. Jackson M.L., Chung J.R., Jackson L.A., Phillips C.H., Benoit J., Monto A.S., et al. Influenza vaccine effectiveness in the United States during the 2015-2016 season. N. Engl. J. Med. 2017; 377(6): 534–43. https://doi.org/10.1056/NEJMoa1700153
  4. Rolfes M.A., Flannery B., Chung J.R., O’Halloran A., Garg S., Belongia E.A., et al. Effects of influenza vaccination in the United States during the 2017-2018 influenza season. Clin. Infect. Dis. 2019; 69(11): 1845–53. https://doi.org/10.1093/cid/ciz075
  5. Doyle J.D., Chung J.R., Kim S.S., Gaglani M., Raiyani C., Zimmerman R.K., et al. Interim estimates of 2018-2019 seasonal influenza vaccine effectiveness – United States. MMWR. Morb. Mortal. Wkly Rep. 2019; 68(6): 135–9. https://doi.org/10.15585/mmwr.mm6806a2
  6. Zost S.J., Parkhouse K., Gumina M.E., Kim K., Diaz P.S., Wilson P.C., et al. Contemporary H3N2 influenza viruses have a glycosylation site that alters binding of antibodies elicited by egg-adapted vaccine strains. Proc. Natl Acad. Sci. USA. 2017; 114(47): 12578–83. https://doi.org/10.1073/pnas.1712377114
  7. Gouma S., Weirick M., Hensley S.E. Antigenic assessment of the H3N2 component of the 2019-2020 northern hemisphere influenza vaccine. Nat. Commun. 2020; 11(1): 2445. https://doi.org/10.1038/s41467-020-16183-y
  8. Cobey S., Gouma S., Parkhouse K., Chambers B.S., Ertl H.C., Schmader K.E., et al. Poor immunogenicity, not vaccine strain egg adaptation, may explain the low H3N2 influenza vaccine effectiveness in 2012-2013. Clin. Infect. Dis. 2018; 67(3): 327–33. https://doi.org/10.1093/cid/ciy097
  9. Klingen T.R., Reimering S., Guzmán C.A., McHardy A.C. In silico vaccine strain prediction for human influenza viruses. Trends Microbiol. 2018; 26(2): 119–31. https://doi.org/10.1016/j.tim.2017.09.001
  10. Morris D.H., Gostic K.M., Pompei S., Bedford T., Łuksza M., Neher R.A., et al. Predictive modeling of influenza shows the promise of applied evolutionary biology. Trends Microbiol. 2018; 26(2): 102–18. https://doi.org/10.1016/j.tim.2017.09.004
  11. Timofeeva T.A., Asatryan M.N., Al’tshteyn A.D., Naroditskiy B.S., Gintsburg A.L., Kaverin N.V. Predicting the evolutionary variability of the influenza A virus. Acta Naturae. 2017; 9(3): 48–54. https://doi.org/10.32607/20758251-2017-9-3-48-54 https://elibrary.ru/zqitjr
  12. Boev B.V. Modeling of the epidemic of influenza A(H1N1) in Russia season 2009-2010. Epidemiologiya i vaktsinoprofilaktika. 2010; (1): 52–8. https://elibrary.ru/laedxn (in Russian)
  13. Huddleston J., Barnes J.R., Rowe T., Kondor R., Wentworth D.E., Whittaker L., et al. Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution. eLife. 2020; 9: e60067. https://doi.org/10.7554/eLife.60067
  14. CDC. Center for Disease Control and Prevention. Influenza (Flu). Available at: https://www.cdc.gov/flu/
  15. Bedford T., Suchard M.A., Lemey P., Dudas G., Gregory V., Hay A.J., et al. Integrating influenza antigenic dynamics with molecular evolution. eLife. 2014; 3: e01914. https://doi.org/10.7554/eLife.01914
  16. Anderson C.S., McCall P.R., Stern H.A., Yang H., Topham D.J. Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation. BMC Bioinformatics. 2018; 19(1): 51. https://doi.org/10.1186/s12859-018-2042-4
  17. Lee M.S., Chen J.S. Predicting antigenic variants of influenza A/H3N2 viruses. Emerg. Infect. Dis. 2004; 10(8): 1385–90. https://doi.org/10.3201/eid1008.040107
  18. Lees W.D., Moss D.S., Shepherd A.J. A computational analysis of the antigenic properties of haemagglutinin in influenza a H3N2. Bioinformatics. 2010; 26(11): 1403–8. https://doi.org/10.1093/bioinformatics/btq160
  19. Burnet F.M., Lush D. The action of certain surface active agents on viruses. Aust. J. Exp. Biol. Med. Sci. 1940; 18: 141–50.
  20. Archetti I., Horsfall F.L. Persistent antigenic variation of influenza A viruses after incomplete neutralization in ovo with heterologous immune serum. J. Exp. Med. 1950; 92(5): 441–62. https://doi.org/10.1084/jem.92.5.441
  21. Lapedes A., Farber R. The geometry of shape space: application to influenza. J. Theor. Biol. 2001; 212(1): 57–69. https://doi.org/10.1006/jtbi.2001.2347
  22. Smith D.J., Lapedes A.S., de Jong J.C., Bestebroer T.M., Rimmelzwaan G.F., Osterhaus A.D., et al. Mapping the antigenic and genetic evolution of influenza virus. Science. 2004; 305(5682): 371–6. https://doi.org/10.1126/science.1097211
  23. Wiley D.C., Skehel J.J. The structure and function of the hemagglutinin membrane glycoprotein of influenza virus. Annu. Rev. Biochem. 1987; 56: 365–94. https://doi.org/10.1146/annurev.bi.56.070187.002053
  24. Wiley D.C., Wilson I.A., Skehel J.J. Structural identification of the antibody-binding sites of Hong Kong influenza haemagglutinin and their involvement in antigenic variation. Nature. 1981; 289(5796): 373–8. https://doi.org/10.1038/289373a0
  25. Wilson I.A., Cox N.J. Structural basis of immune recognition of influenza virus hemagglutinin. Annu. Rev. Immunol. 1990; 8: 737–71. https://doi.org/10.1146/annurev.iy.08.040190.003513
  26. Liao Y.C., Lee M.S., Ko C.Y., Hsiung C.A. Bioinformatics models for predicting antigenic variants of influenza A/H3N2 virus. Bioinformatics. 2008; 24(4): 505–12. https://doi.org/10.1093/bioinformatics/btm638
  27. Asatryan M.N., Agasaryan V.G, Shcherbinin D.N., Timofeev B.I., Ershov I.F., Shmyr I.S., et al. Influenza IDE. Registration certificate № 2020617965; 2020. (in Russian)
  28. Lawson C.L., Hanson R.J. Solving Least Squares Problems. New Jersey: Englewood Cliffs; 1974.
  29. Khalafyan A.A. Mathematical Statistics with Elements of Probability [Matematicheskaya statistika s elementami teorii veroyatnosti]. Moscow: Binom; 2010. (in Russian)
  30. Stephenson I., Gaines Das R., Wood J.M., Katz J.M. Comparison of neutralising antibody assays for detection of antibody to influenza A/H3N2 viruses: an international collaborative study. Vaccine. 2007; 25(20): 4056–63. https://doi.org/10.1016/j.vaccine.2007.02.039
  31. Wood J.M., Major D., Heath A., Newman R.W., Höschler K., Stephenson I., et al. Reproducibility of serology assays for pandemic influenza H1N1: collaborative study to evaluate a candidate WHO International Standard. Vaccine. 2012; 30(2): 210–7. https://doi.org/10.1016/j.vaccine.2011.11.019
  32. Zacour М., Ward В.J., Brewer A., Tang P., Boivin G., Li Y. Standardization of hemagglutination inhibition assay for influenza serology allows for high reproducibility between laboratories. Clin. Vaccine Immunol. 2016; 23(3): 236–42. https://doi.org/10.1128/CVI.00613-15
  33. The Francis Crick Institute. Worldwide Influenza Centre lab. Annual and interim reports. Available at: https://www.crick.ac.uk/research/platforms-and-facilities/worldwide-influenza-centre/annual-and-interim-reports
  34. DuPai C.D., McWhite C.D., Smith C.B., Garten R., Maurer-Stroh S., Wilke C.O. Influenza passaging annotations: what they tell us and why we should listen. Virus Evol. 2019; 5(1): vez016. https://doi.org/10.1093/ve/vez016
  35. Wu N.C., Zost S.J., Thompson A.J., Oyen D., Nycholat C.M., McBride R., et al. A structural explanation for the low effectiveness of the seasonal influenza H3N2 vaccine. PLoS Pathog. 2017; 13(10): e1006682. https://doi.org/10.1371/journal.ppat.1006682
  36. Park Y.W., Kim Y.H., Jung H.U., Jeong O.S., Hong E.J., Kim H. Comparison of antigenic mutation during egg and cell passage cultivation of H3N2 influenza virus. Clin. Exp. Vaccine Res. 2020; 9(1): 56–63. https://doi.org/10.7774/cevr.2020.9.1.56
  37. Popova L., Smith K., West A.H., Wilson P.C., James J.A., Thompson L.F. Immunodominance of antigenic site B over site A of hemagglutinin of recent H3N2 influenza viruses. PLoS One. 2012; 7(7): e41895. https://doi.org/10.1371/journal.pone.0041895
  38. Klein N.P., Fireman B., Goddard K., Zerbo O., Asher J., Zhou J. Vaccine effectiveness of cell-culture relative to egg-based inactivated influenza vaccine during the 2017-2018 influenza season. PLoS One. 2020; 15(2): e0229279. https://doi.org/10.1371/journal.pone.0229279
  39. GISAID. Eurosurveillance; 2017. Available at: https://gisaid.org/resources/commentary-on-gisaid/
  40. Smith T.F., Waterman M.S. Identification of common molecular subsequences. J. Mol. Biol. 1981; 147(1): 195–7. https://doi.org/10.1016/0022-2836(81)90087-5
  41. GetArea. Available at: http://curie.utmb.edu/getarea.html
  42. RCSB PDB: Homepage. Available at: https://www.rcsb.org/
  43. Shcherbinin D.N., Alekseeva S.V., Shmarov M.M., Smirnov Yu.A., Naroditskiy B.S., Gintsburg A.L. The analysis of B-cell epitopes of influenza virus hemagglutinin. Acta Naturae. 2016; 8(1): 13–20. https://doi.org/10.32607/20758251-2016-8-1-13-20 https://elibrary.ru/vsnklb
  44. A standardised numbering for all subtypes of Influenza A hemaggluttin (HA) sequences based on the mature HA sequence. Available at: https://antigenic-cartography.org/surveillance/evergreen/HAnumbering/
  45. Smith D.J., Forrest S., Hightower R.R., Perelson A.S. Deriving shape space parameters from immunological data. J. Theor. Biol. 1997; 189(2): 141–50. https://doi.org/10.1006/jtbi.1997.0495
  46. Kilbourne E.D., ed. The Influenza Viruses and Influenza. London: Academic Press Inc.; 1975.

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3. Figure. Functions for evaluating the degree of homology.

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Copyright (c) 2023 Asatryan M.N., Timofeev B.I., Shmyr I.S., Khachatryan K.R., Shcherbinin D.N., Timofeeva T.A., Gerasimuk E.R., Agasaryan V.G., Ershov I.F., Shashkova T.I., Kardymon O.L., Ivanisenko N.V., Semenenko T.A., Naroditsky B.S., Logunov D.Y., Gintsburg A.L.

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