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...

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Main Authors: Karsten Jürchott, Axel Ronald Schulz, Cecilia Bozzetti, Dominika Pohlmann, Ulrik Stervbo, Sarah Warth, Julia Nora Mälzer, Julian Waldner, Brunhilde Schweiger, Sven Olek, Andreas Grützkau, Nina Babel, Andreas Thiel, Avidan Uriel Neumann
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2016-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4782986?pdf=render
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spelling doaj-6479b733f8094104aa598361219791db2020-11-25T01:58:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01113e015081210.1371/journal.pone.0150812Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination.Karsten JürchottAxel Ronald SchulzCecilia BozzettiDominika PohlmannUlrik StervboSarah WarthJulia Nora MälzerJulian WaldnerBrunhilde SchweigerSven OlekAndreas GrützkauNina BabelAndreas ThielAvidan Uriel NeumannUnderstanding 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.http://europepmc.org/articles/PMC4782986?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Karsten Jürchott
Axel Ronald Schulz
Cecilia Bozzetti
Dominika Pohlmann
Ulrik Stervbo
Sarah Warth
Julia Nora Mälzer
Julian Waldner
Brunhilde Schweiger
Sven Olek
Andreas Grützkau
Nina Babel
Andreas Thiel
Avidan Uriel Neumann
spellingShingle Karsten Jürchott
Axel Ronald Schulz
Cecilia Bozzetti
Dominika Pohlmann
Ulrik Stervbo
Sarah Warth
Julia Nora Mälzer
Julian Waldner
Brunhilde Schweiger
Sven Olek
Andreas Grützkau
Nina Babel
Andreas Thiel
Avidan Uriel Neumann
Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination.
PLoS ONE
author_facet Karsten Jürchott
Axel Ronald Schulz
Cecilia Bozzetti
Dominika Pohlmann
Ulrik Stervbo
Sarah Warth
Julia Nora Mälzer
Julian Waldner
Brunhilde Schweiger
Sven Olek
Andreas Grützkau
Nina Babel
Andreas Thiel
Avidan Uriel Neumann
author_sort Karsten Jürchott
title 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_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_sort highly predictive model for a protective immune response to the a(h1n1)pdm2009 influenza strain after seasonal vaccination.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2016-01-01
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.
url http://europepmc.org/articles/PMC4782986?pdf=render
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