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Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination
Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion r...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4782986/ https://www.ncbi.nlm.nih.gov/pubmed/26954292 http://dx.doi.org/10.1371/journal.pone.0150812 |
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author | Jürchott, Karsten Schulz, Axel Ronald Bozzetti, Cecilia Pohlmann, Dominika Stervbo, Ulrik Warth, Sarah Mälzer, Julia Nora Waldner, Julian Schweiger, Brunhilde Olek, Sven Grützkau, Andreas Babel, Nina Thiel, Andreas Neumann, Avidan Uriel |
author_facet | Jürchott, Karsten Schulz, Axel Ronald Bozzetti, Cecilia Pohlmann, Dominika Stervbo, Ulrik Warth, Sarah Mälzer, Julia Nora Waldner, Julian Schweiger, Brunhilde Olek, Sven Grützkau, Andreas Babel, Nina Thiel, Andreas Neumann, Avidan Uriel |
author_sort | Jürchott, Karsten |
collection | PubMed |
description | Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4(+) T cells, and amongst them particularly naive CD4(+) T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4(+) T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4(+) counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31–50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count. |
format | Online Article Text |
id | pubmed-4782986 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-47829862016-03-23 Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination Jürchott, Karsten Schulz, Axel Ronald Bozzetti, Cecilia Pohlmann, Dominika Stervbo, Ulrik Warth, Sarah Mälzer, Julia Nora Waldner, Julian Schweiger, Brunhilde Olek, Sven Grützkau, Andreas Babel, Nina Thiel, Andreas Neumann, Avidan Uriel PLoS One Research Article Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4(+) T cells, and amongst them particularly naive CD4(+) T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4(+) T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4(+) counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31–50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count. Public Library of Science 2016-03-08 /pmc/articles/PMC4782986/ /pubmed/26954292 http://dx.doi.org/10.1371/journal.pone.0150812 Text en © 2016 Jürchott et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Jürchott, Karsten Schulz, Axel Ronald Bozzetti, Cecilia Pohlmann, Dominika Stervbo, Ulrik Warth, Sarah Mälzer, Julia Nora Waldner, Julian Schweiger, Brunhilde Olek, Sven Grützkau, Andreas Babel, Nina Thiel, Andreas Neumann, Avidan Uriel Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination |
title | Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination |
title_full | Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination |
title_fullStr | Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination |
title_full_unstemmed | Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination |
title_short | Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination |
title_sort | highly predictive model for a protective immune response to the a(h1n1)pdm2009 influenza strain after seasonal vaccination |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4782986/ https://www.ncbi.nlm.nih.gov/pubmed/26954292 http://dx.doi.org/10.1371/journal.pone.0150812 |
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