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Immunosenescence is a hallmark of the aging immune system and is considered the main cause of a reduced vaccine efficacy in the elderly. Although γδ T cells can become activated by recombinant influenza hemagglutinin, their age-related immunocompetence during a virus-induced immune response has so far not been investigated. In this study we evaluate the kinetics of γδ T cells after vaccination with the trivalent 2011/2012 northern hemisphere seasonal influenza vaccine. We applied multi-parametric flow cytometry to a cohort of 21 young (19-30 years) and 23 elderly (53-67 years) healthy individuals. Activated and proliferating γδ T cells, as identified by CD38 and Ki67 expression, were quantified on the days 0, 3, 7, 10, 14, 17, and 21. We observed a significantly lower number of activated and proliferating γδ T cells at baseline and following vaccination in elderly as compared to young individuals. The kinetics changes of activated γδ T cells were much stronger in the young, while corresponding changes in the elderly occurred slower. In addition, we observed an association between day 21 HAI titers of influenza A and the frequencies of Ki67+ γδ T cells at day 7 in the young. In conclusion, aging induces alterations of the γδ T cell response that might have negative implications for vaccination efficacy.

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.