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Implementing sequence-based antigenic distance calculation into immunological shape space model.

Christopher S Anderson1, Mark Y Sangster2, Hongmei Yang3

  • 1Department of Pediatrics, University of Rochester Medical Center, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA. christopher_anderson@urmc.rochester.edu.

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Summary

Pre-existing immunity shapes antibody responses to influenza vaccines. This study developed a computational model to predict how prior exposure to different flu strains affects vaccine-induced antibody specificity and cross-reactivity, validating it with 2009 H1N1 pandemic data.

Keywords:
2009 pandemicAntigenic distanceAntigenic sitesArtificial immune systemsComputational immunologyEpitopesGillespie algorithmH1N1HAHemagglutininHumoral immune systemInfluenzaShape spaceSimulationsStalkStemVaccinespH1N1

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Area of Science:

  • Immunology
  • Computational Biology
  • Vaccinology

Background:

  • The 2009 H1N1 influenza pandemic necessitated rapid vaccine deployment.
  • Antibody responses to influenza vaccines are influenced by an individual's pre-existing immunity.
  • Differences in antibody specificity and cross-reactivity arise due to prior exposure to influenza strains.

Purpose of the Study:

  • To develop a computational model predicting influenza vaccine responses based on pre-existing immunity.
  • To assess the model's ability to forecast antibody specificity and cross-reactivity.
  • To understand how prior influenza exposure impacts vaccine-induced immunity.

Main Methods:

  • Development of a computational model to simulate antibody responses to influenza hemagglutinin (HA) protein.
  • Modeling the effect of pre-existing immunity on antibody targeting and cross-reactivity.
  • Validation of the model using data from human subjects vaccinated with the 2009 H1N1 vaccine.

Main Results:

  • Pre-existing immunity significantly impacts antibody specificity and cross-reactivity to influenza vaccines.
  • The antigenic relatedness between prior exposure and vaccine strains influences immunodominance.
  • Increased antibody cross-reactivity was observed when pre-existing immunity was distinct from the vaccine strain.
  • Model simulations showed qualitative and quantitative agreement with human immune responses.

Conclusions:

  • A novel computational method can predict antibody responses post-influenza vaccination, considering individual exposure histories.
  • The model accurately reflects human immune responses to the 2009 H1N1 pandemic vaccine.
  • Variations in antibody cross-reactivity are expected in individuals with diverse influenza exposure backgrounds.