Implementing sequence-based antigenic distance calculation into immunological shape space model
Abstract Background In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza “swine flu” pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computat...
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doaj-3a22b7dfc3144b44a88af1e86ed327852020-11-25T03:06:35ZengBMCBMC Bioinformatics1471-21052020-06-0121111310.1186/s12859-020-03594-3Implementing sequence-based antigenic distance calculation into immunological shape space modelChristopher S. Anderson0Mark Y. Sangster1Hongmei Yang2Thomas J. Mariani3Sidhartha Chaudhury4David J. Topham5Department of Pediatrics, University of Rochester Medical Center, University of Rochester School of Medicine and DentistryNew York Influenza Center of Excellence at David Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester School of Medicine and DentistryDepartment of Biostatistics and Computational Biology, University of Rochester Medical CenterDepartment of Pediatrics, University of Rochester Medical Center, University of Rochester School of Medicine and DentistryCenter for Enabling Capabilities, Walter Reed Army Institute of ResearchNew York Influenza Center of Excellence at David Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester School of Medicine and DentistryAbstract Background In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza “swine flu” pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. Results We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual’s pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. Conclusion We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.http://link.springer.com/article/10.1186/s12859-020-03594-3Gillespie algorithmShape spaceAntigenic distanceEpitopesAntigenic sitesHemagglutinin |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Christopher S. Anderson Mark Y. Sangster Hongmei Yang Thomas J. Mariani Sidhartha Chaudhury David J. Topham |
spellingShingle |
Christopher S. Anderson Mark Y. Sangster Hongmei Yang Thomas J. Mariani Sidhartha Chaudhury David J. Topham Implementing sequence-based antigenic distance calculation into immunological shape space model BMC Bioinformatics Gillespie algorithm Shape space Antigenic distance Epitopes Antigenic sites Hemagglutinin |
author_facet |
Christopher S. Anderson Mark Y. Sangster Hongmei Yang Thomas J. Mariani Sidhartha Chaudhury David J. Topham |
author_sort |
Christopher S. Anderson |
title |
Implementing sequence-based antigenic distance calculation into immunological shape space model |
title_short |
Implementing sequence-based antigenic distance calculation into immunological shape space model |
title_full |
Implementing sequence-based antigenic distance calculation into immunological shape space model |
title_fullStr |
Implementing sequence-based antigenic distance calculation into immunological shape space model |
title_full_unstemmed |
Implementing sequence-based antigenic distance calculation into immunological shape space model |
title_sort |
implementing sequence-based antigenic distance calculation into immunological shape space model |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2020-06-01 |
description |
Abstract Background In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza “swine flu” pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. Results We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual’s pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. Conclusion We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories. |
topic |
Gillespie algorithm Shape space Antigenic distance Epitopes Antigenic sites Hemagglutinin |
url |
http://link.springer.com/article/10.1186/s12859-020-03594-3 |
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