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
Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.
Institute for Theoretical Biology, Humboldt University, Berlin, Germany.
PLoS One. 2016 Mar 8;11(3):e0150812. doi: 10.1371/journal.pone.0150812. eCollection 2016.
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.
对于即将到来的流感大流行以及优化高危人群（尤其是老年人）的季节性疫苗接种策略而言，了解针对新型流感毒株接种疫苗后的免疫反应至关重要。最近有人提出了预测季节性疫苗中三种毒株间最佳血清转化反应的模型。然而，这些模型使用了大量变量和/或接种疫苗后的信息。在此项探索性初步研究中，我们分析了针对新出现的甲型（H1N1）pdm09流感病毒毒株血清学阴性的年轻（<31岁，N = 17）与年长（≥50岁，N = 20）献血者的基线免疫状态，并将其与季节性流感疫苗接种后针对该特定毒株的血清学反应进行关联。采用广泛的多色流式细胞术分析（检测36个淋巴细胞亚群）来定量评估接种疫苗前的细胞免疫状态。我们确定CD4 + T细胞，尤其是其中的初始CD4 + T细胞，是成功的甲型（H1N1）pdm09免疫反应的最佳关联指标。此外，正如预期的那样，除年龄外，献血者在基线时血清学阴性的流感毒株数量（NSSN）也是重要的预测因素。年龄、NSSN和基线CD4 + T细胞计数共同对甲型（H1N1）pdm09的血清保护作用（血凝抑制试验≥40）具有89%的高精度预测能力（p值 = 0.00002）。另一项验证研究（N = 43名针对甲型（H1N1）pdm09血清学阴性的疫苗接种者）证实了年龄、NSSN和基线CD4计数的预测价值（准确率 = 85%，p值 = 0.0000004）。此外，纳入31 - 50岁的献血者表明，年龄预测功能并非与年龄呈线性关系，而是呈中点约为50岁的S形曲线关系。利用这些结果，我们提出了一个具有临床相关性的预测模型，该模型将季节性多价疫苗接种后对甲型（H1N1）pdm09流感毒株无保护作用的概率作为年龄、NSSN和基线CD4计数的连续函数给出。
