Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model

H1N1 is the earliest emerging subtype of influenza A viruses with available genomic sequences, has caused several pandemics and seasonal epidemics, resulting in millions of deaths and enormous economic losses. Timely determination of new antigenic variants is crucial for the vaccine selection and flu prevention. In this study, we chronologically divided the H1N1 strains into several periods in terms of the epidemics and pandemics. Computational models have been constructed to predict antigenic variants based on epidemic and pandemic periods. By sequence analysis, we demonstrated the diverse mutation patterns of HA1 protein on different periods and that an individual model built upon each period can not represent the variations of H1N1 virus. A stacking model was established for the prediction of antigenic variants, combining all the variation patterns across periods, which would help assess a new influenza strain’s antigenicity. Three different feature extraction methods, i.e. residue-based, regional band-based and epitope region-based, were applied on the stacking model to verify its feasibility and robustness. The results showed the capability of determining antigenic variants prediction with accuracy as high as 0.908 which performed better than any of the single models. The prediction performance using the stacking model indicates clear distinctions of mutation patterns and antigenicity between epidemic and pandemic strains. It would also facilitate rapid determination of antigenic variants and influenza surveillance.

[1]  G. K. Hirst.  THE QUANTITATIVE DETERMINATION OF INFLUENZA VIRUS AND ANTIBODIES BY MEANS OF RED CELL AGGLUTINATION , 1942, The Journal of experimental medicine.

[2]  T. Uyeki,et al.  Prevention and control of influenza: recommendations of the Advisory Committee on Immunization Practices (ACIP), 2008. , 2008, MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports.

[3]  Prevention and control of influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices (ACIP)--United States, 2012-13 influenza season. , 2012, MMWR. Morbidity and mortality weekly report.

[4]  David J Stevens,et al.  Structure of influenza hemagglutinin in complex with an inhibitor of membrane fusion , 2008, Proceedings of the National Academy of Sciences.

[5]  Trevor Bedford,et al.  Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses , 2015, Proceedings of the National Academy of Sciences.

[6]  Aiping Wu,et al.  Antigenic Patterns and Evolution of the Human Influenza A (H1N1) Virus , 2015, Scientific Reports.

[7]  Trevor Bedford,et al.  Global circulation patterns of seasonal influenza viruses vary with antigenic drift , 2015, Nature.

[8]  W. Fitch,et al.  Predicting the evolution of human influenza A. , 1999, Science.

[9]  Zhiwei Cao,et al.  Incorporating structure context of HA protein to improve antigenicity calculation for influenza virus A/H3N2 , 2016, Scientific Reports.

[10]  Aiping Wu,et al.  Mapping of H3N2 influenza antigenic evolution in China reveals a strategy for vaccine strain recommendation , 2012, Nature Communications.

[11]  Tong Zhang,et al.  Using Sequence Data To Infer the Antigenicity of Influenza Virus , 2013, mBio.

[12]  Wilfred Ndifon,et al.  On the use of hemagglutination-inhibition for influenza surveillance: surveillance data are predictive of influenza vaccine effectiveness. , 2009, Vaccine.

[13]  Zhipeng Cai,et al.  AntigenMap 3D: an online antigenic cartography resource , 2012, Bioinform..

[14]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[15]  Jonathan Dushoff,et al.  Codon bias and frequency-dependent selection on the hemagglutinin epitopes of influenza A virus , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Fransiskus Xaverius Ivan,et al.  Identification of Potential Critical Virulent Sites Based on Hemagglutinin of Influenza a Virus in Past Pandemic Strains , 2017, BIOINFORMATICS 2017.

[17]  Adrian J. Shepherd,et al.  A computational analysis of the antigenic properties of haemagglutinin in influenza A H3N2 , 2010, Bioinform..

[18]  A. Osterhaus,et al.  Haemagglutination-inhibiting antibody to influenza virus. , 2003, Developments in biologicals.

[19]  Kenli Li,et al.  A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures , 2017, Scientific Reports.

[20]  Gleider Hernández,et al.  Effectiveness of vaccines in preventing hospitalization due to COVID-19: A multicenter hospital-based case-control study, Germany, June 2021 to January 2022 , 2022, Vaccine.

[21]  Emmie de Wit,et al.  Emerging influenza. , 2008, Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology.

[22]  I. Wilson,et al.  Structural basis of immune recognition of influenza virus hemagglutinin. , 1990, Annual review of immunology.

[23]  Niall Johnson,et al.  Updating the Accounts: Global Mortality of the 1918-1920 "Spanish" Influenza Pandemic , 2002, Bulletin of the history of medicine.

[24]  Edward C. Holmes,et al.  Different Evolutionary Trajectories of European Avian-Like and Classical Swine H1N1 Influenza A Viruses , 2009, Journal of Virology.

[25]  Malbea A Lapete,et al.  Morbidity and Mortality Weekly Report Prevention and Control of Influenza Recommendations of the Advisory Committee on Immunization Practices (acip) Centers for Disease Control and Prevention Epidemiology Program Office Early Release 1 Prevention and Control of Influenza Recommendations of the Advis , 2022 .

[26]  Jacob Benesty,et al.  Noise Reduction in Speech Processing , 2009 .

[27]  Pingan He,et al.  Predicting influenza antigenicity from Hemagglutintin sequence data based on a joint random forest method , 2017, Scientific Reports.

[28]  A. Vincent,et al.  Genetic and antigenic characterization of H1 influenza viruses from United States swine from 2008 , 2011, The Journal of general virology.

[29]  Updated interim influenza vaccination recommendations--2004-05 influenza season. , 2004, MMWR. Morbidity and mortality weekly report.

[30]  M. Deem,et al.  The epitope regions of H1-subtype influenza A, with application to vaccine efficacy , 2009, Protein engineering, design & selection : PEDS.

[31]  A. Lapedes,et al.  Mapping the Antigenic and Genetic Evolution of Influenza Virus , 2004, Science.

[32]  David F. Burke,et al.  A Recommended Numbering Scheme for Influenza A HA Subtypes , 2014, PloS one.

[33]  M. Lässig,et al.  A predictive fitness model for influenza , 2014, Nature.

[34]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[35]  Yuelong Shu,et al.  GISAID: Global initiative on sharing all influenza data – from vision to reality , 2017, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[36]  K. Katoh,et al.  MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability , 2013, Molecular biology and evolution.

[37]  G. Boivin,et al.  Standardization of Hemagglutination Inhibition Assay for Influenza Serology Allows for High Reproducibility between Laboratories , 2016, Clinical and Vaccine Immunology.

[38]  E. Brown,et al.  Influenza virus genetics. , 2000, Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie.

[39]  Abdoelnaser M. Degoot,et al.  Predicting Antigenicity of Influenza A Viruses Using biophysical ideas , 2019, Scientific Reports.

[40]  Xiping Shen,et al.  Computational Identification of Antigenicity-Associated Sites in the Hemagglutinin Protein of A/H1N1 Seasonal Influenza Virus , 2015, PloS one.

[41]  Evan E. Bolton,et al.  Database resources of the National Center for Biotechnology Information , 2011, Nucleic acids research.

[42]  Chao A. Hsiung,et al.  Bioinformatics models for predicting antigenic variants of influenza A/H3N2 virus , 2008, Bioinform..

[43]  Richard A. Goldstein,et al.  Changing Selective Pressure during Antigenic Changes in Human Influenza H3 , 2008, PLoS pathogens.

[44]  R. Wolfinger,et al.  Stacked Ensemble Models for Improved Prediction Accuracy , 2017 .

[45]  J. Skehel,et al.  H1 and H7 influenza haemagglutinin structures extend a structural classification of haemagglutinin subtypes. , 2004, Virology.