Drivers of individual variation in influenza vaccine response and protection from infection

项目摘要 通过接种疫苗诱导保护性免疫应答对于许多疾病的管理至关重要。 病原体对于抗原可变的病原体，如流感，保护性免疫应答施加了主要的 对病毒种群的选择压力，并间接影响疫苗株的选择和疫苗 有效性我们对流感保护性免疫力的产生和维持缺乏了解 阻碍了疫苗的开发和进化预测的准确性。血凝素抗体滴度 (HA)50年前，表面蛋白被确定为保护相关物，最近的证据表明， 许多抗HA抗体直接或间接地有助于病毒中和。然而，HA滴度仅保持在 中度预测个体暴露后感染的风险，以及其他免疫系统的贡献 反应不太清楚。除了了解保护的相关因素外， 提高病毒适应性预测的准确性，并为疫苗开发提供可靠的终点。在这里， 我们提出了一些补充方法，以确定保护免受感染的相关因素和驱动因素 和疫苗应答的异质性。我们将整合各种变量，包括感染和疫苗接种 病史、基线抗原特异性和抗原不可知免疫状态、内在特征（包括年龄）， 性别和体重来预测对流感疫苗接种的反应，并提取机理见解。为了 为了实现我们的具体目标，我们将利用现有的免疫参数纵向研究数据， 在人类感染流感病毒和接种疫苗后。首先，我们将使用计算和多模态 单细胞方法研究疫苗接种和感染如何影响宿主免疫状态。新兴 包括我们自己的数据在内的证据表明，疫苗接种和感染可以建立新的抗原不可知性， 影响未来疫苗反应的免疫设定点。接下来，我们建议整合互补性 计算方法，跨越机器学习，因果中介分析和机械建模， 预测和发展对疫苗反应性的因果机制见解，以及预防严重和轻度 感染我们将开发和分发一套配套工具来实现这些新颖的方法 实验室和计算生物学家都可以使用。改善对免疫反应的预测，特别是 保护性免疫反应，可能导致更有效的疫苗接种策略，减轻疫苗失败， 不同亚群的流感疫苗接种，并改善流感疫苗接种对公共卫生的影响。的方法和工具 可以提供基本的框架来剖析对其他疫苗和病原体的反应。

PROJECT SUMMARY The induction of protective immune responses through vaccination is central to the management of many pathogens. For antigenically variable pathogens such as influenza, protective immune responses impose a major selective pressure on viral populations and indirectly influence vaccine strain selection and vaccine effectiveness. Our poor understanding of the generation and maintenance of protective immunity to influenza hinders vaccine development and the accuracy of evolutionary forecasts. Antibody titers to the hemagglutinin (HA) surface protein were established as a correlate of protection 50 years ago, and more recent evidence shows many anti-HA antibodies directly and indirectly contribute to viral neutralization. However, HA titers remain only moderately predictive of an individual’s risk of infection on exposure, and the contributions of other immune responses are less well understood. Understanding the causes in addition to correlates of protection could increase the accuracy of forecasts of viral fitness and provide reliable endpoints for vaccine development. Here, we propose complementary approaches to identify the correlates and drivers underlying protection from infection and heterogeneity in vaccine responses. We will integrate diverse variables, including infection and vaccination history, baseline antigen-specific and antigen-agnostic immune states, intrinsic characteristics including age, sex, and body mass to predict responses to influenza vaccination and extract mechanistic insight. In order to address our specific aims, we will leverage data from existing, longitudinal studies of immune parameters following influenza virus infections and vaccination in humans. First we will use computational and multimodal single-cell approaches to investigate how vaccination and infection impact host immune status. Emerging evidence, including our own data, suggests that vaccination and infection can establish new antigen-agnostic immune set points that affect future vaccine responses. Next we propose to integrate complementary computational approaches, spanning machine learning, causal mediation analysis, and mechanistic modeling to predict and develop causal mechanistic insight into vaccine responsiveness and protection from severe and mild infection. We will develop and distribute a suite of accompanying tools to make these novel approaches accessible to bench and computational biologists. Improved prediction of immune responses, especially protective immune responses, could lead to more effective vaccination strategies that mitigate vaccine failure in different subpopulations and improve the public health impact of influenza vaccination. The methods and tools that we develop can provide foundational frameworks to dissect responses to other vaccines and pathogens.