Integrative mapping of pre-existing influenza immune landscapes predicts vaccine response.

Stephanie Hao,Ivan Tomic,Benjamin B Lindsey,Ya Jankey Jagne,Katja Hoschler,Adam Meijer,Juan Manuel Carreño Quiroz,Philip Meade,Kaori Sano,Chikondi Peno,André G Costa-Martins,Debby Bogaert,Beate Kampmann,Helder Nakaya,Florian Krammer,Thushan I de Silva,Adriana Tomic
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Abstract

BACKGROUND Predicting individual vaccine responses is a substantial public health challenge. We developed immunaut, an open-source, data-driven framework for systems vaccinologists to analyze and predict immunological outcomes across diverse vaccination settings, beyond traditional assessments. METHODS Using a comprehensive live attenuated influenza vaccine (LAIV) dataset from 244 Gambian children, immunaut integrated pre- and post-vaccination humoral, mucosal, cellular, and transcriptomic data. Through advanced modeling, our framework provided a holistic, systems-level view of LAIV-induced immunity. RESULTS The analysis identified three distinct immunophenotypic profiles driven by baseline immunity: (1) CD8 T-cell responders with strong pre-existing immunity boosting memory T-cell responses; (2) Mucosal responders with prior influenza A virus immunity developing robust mucosal IgA and subsequent influenza B virus seroconversion; and (3) Systemic, broad influenza A virus responders starting from immune naivety who mounted broad systemic antibody responses. Pathway analysis revealed how pre-existing immune landscapes and baseline features, such as mucosal preparedness and cellular support, quantitatively dictate vaccine outcomes. CONCLUSION Our findings emphasize the power of integrative, predictive frameworks for advancing precision vaccinology. The immunaut framework is a valuable resource for deciphering vaccine response heterogeneity and can be applied to optimize immunization strategies across diverse populations and vaccine platforms. FUNDING Wellcome Trust (110058/Z/15/Z); Bill & Melinda Gates Foundation (INV-004222); HIC-Vac consortium; NIAID (R21 AI151917); NIAID CEIRR Network (75N93021C00045).
预先存在的流感免疫景观的综合制图预测疫苗反应。
背景:预测个体疫苗反应是一项重大的公共卫生挑战。我们开发了immunaut,这是一个开源的、数据驱动的框架,供系统疫苗学家分析和预测不同疫苗接种环境下的免疫结果,超越传统的评估。方法使用来自244名冈比亚儿童的综合减毒流感活疫苗(LAIV)数据集,综合免疫接种前后的体液、粘膜、细胞和转录组学数据。通过高级建模,我们的框架提供了laiv诱导免疫的整体系统级视图。结果该分析确定了基线免疫驱动的三种不同的免疫表型:(1)CD8 t细胞应答者具有强大的预先免疫增强记忆t细胞应答;(2)先前具有甲型流感病毒免疫的粘膜应答者产生强大的粘膜IgA和随后的乙型流感病毒血清转化;(3)系统的,广泛的甲型流感病毒应答者,从免疫幼稚开始,他们产生了广泛的系统抗体应答。途径分析揭示了预先存在的免疫景观和基线特征(如粘膜准备和细胞支持)如何定量地决定疫苗结果。结论:我们的研究结果强调了综合预测框架在推进精准疫苗学方面的作用。免疫框架是破译疫苗反应异质性的宝贵资源,可用于优化不同人群和疫苗平台的免疫策略。基金惠康信托基金(110058/Z/15/Z);比尔和梅林达·盖茨基金会(INV-004222);HIC-Vac财团;Niaid (r21 ai151917);NIAID CEIRR网络(75N93021C00045)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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