Immune Responses to Influenza Vaccination

精确模型的发展使我们能够预测对扰动的生物反应，这有可能增加我们对病理生理学的机械理解，并有助于改进的治疗方法的发展。人类免疫系统为开发这种系统生物学方法提供了极好的环境：许多免疫细胞和分子成分很容易从血液中获得，允许跨多个时间点从个人身上收集样本，随后进行深入的数据生成和分析。此外，人们越来越多地认识到免疫系统和炎症在多发性疾病的发病机制中起到了作用。这些疾病不仅包括传统上被认为与免疫系统有关的疾病，如自身免疫和传染病，还包括癌症、心脏病、糖尿病、肥胖症、神经退行性疾病和其他影响到很大一部分人口的慢性疾病(Germain和Schwartzberg，Nat免疫。2011年)。因此，更全面和定量地了解免疫反应是如何编排的，以及识别有效与破坏性反应的预测性分子和细胞参数，可能对预防和治疗各种疾病具有重要意义。为此，我协调了美国国立卫生研究院人类免疫学中心的一项初步研究，旨在帮助建立正常人类变异(人类免疫组)的数据库，并了解免疫状态的变化如何有助于免疫反应和疾病(曾，Schwartzberg等人，Cell 2014；Dickler，H.等人，Ann New York Acad Sci，2013)。 作为建立人类免疫模型的第一步，我们深入分析了基线和对流感疫苗接种扰动的反应的免疫参数。对63名个体接种前后的外周血细胞转录物、血清细胞因子、流感滴度、126个细胞亚群的频率和B细胞反应进行了评估，并用其开发了一个计算框架，以剖析个体间和个体内的差异，并建立疫苗接种后抗体反应的预测模型。与其他疫苗研究类似，我们已经能够显示疫苗接种后与疫苗反应相关的基因表达特征，但进一步将这些特征与B细胞浆母细胞种群的扩大联系起来。重要的是，使用一种考虑了先前存在的血清学、年龄、种族和性别的影响的方法，我们证明了疫苗接种后对流感疫苗的大部分反应和预测性签名很大程度上受到疫苗接种前滴度的影响。引人注目的是，我们发现，与年龄和预先存在的抗体效价无关，仅使用预扰动参数就可以构建准确的模型，这些参数使用来自独立基线时间点的数据进行验证。有助于预测的大多数参数描述了个体间免疫细胞种群的时间稳定基线差异，提高了干预前免疫健康监测的前景(曾，施瓦茨伯格等，Cell 2014)。我们详细描述的框架为研究人类免疫在健康和疾病中的作用提供了一个潜在的资源。 去年，我们一直在进行一项类似的项目，比较H5N1流感无佐剂疫苗和佐剂疫苗的反应。为了补充我们的工作，我们增加了新的滤泡辅助T细胞的功能分析和表征，滤泡辅助T细胞是帮助B细胞进行长期抗体反应的关键T细胞群。我们正在使用这些方法来更深入地研究对免疫的反应，并优化技术和科学方法，以更好地理解疫苗。

The development of accurate models that permit prediction of biological responses upon perturbation has the potential to increase our mechanistic understanding of pathophysiology and contribute to the development of improved therapeutics. The human immune system provides an excellent context for developing such systems biology approaches: many immune cells and molecular components are readily accessible from blood, permitting collection of samples from individuals across multiple time-points, followed by in depth data generation and analyses. Furthermore, there is an increasing understanding that the immune system and inflammation contribute to the pathogenesis of multiple disorders. These include not only those classically considered to involve the immune system such as autoimmune and infectious diseases, but also cancer, cardiac disease, diabetes, obesity, neurodegeneration, and other chronic illnesses affecting a large segment of the population (Germain and Schwartzberg, Nat Immunol. 2011). Thus, a more comprehensive and quantitative understanding of how immune responses are orchestrated, together with identification of predictive molecular and cellular parameters of effective vs. damaging responses, could have major implications for the prevention and treatment of diverse diseases. To this end, I coordinated one of the initial studies from the NIH Center for Human Immunology designed to help build a data base of normal human variation (the human immunome) and understand how variation in immune states contributes to immune reponses and disease (Tsang, Schwartzberg et al, Cell 2014; DIckler, H. et al, Ann New York Acad Sci, 2013). As a first step towards modeling human immunity, we have analyzed immune parameters in depth both at baseline and in response to perturbation with influenza vaccination. Peripheral blood cell transcriptomes, serum cytokines, influenza titers, frequencies of 126 cell subpopulations, and B cell responses were assessed before and after vaccination in 63 individuals and used to develop a computational framework to dissect inter- and intra-individual variation and build predictive models of post-vaccination antibody responses. Similar to other vaccine studies we have been able to show post-vaccination gene expression signatures that correlated with vaccine responses, but furthermore have linked these to the expansion of B cell plasmablast populations. Importantly, using an approach that accounts for the influence of pre-existing serology, age, ethnicity and gender, we demonstrated that much of the post-vaccination responses to Influenza vaccination and predictive signatures are heavily influenced by pre-vaccination titers. Strikingly, independent of age and pre-existing antibody titers, we found that accurate models could be constructed using pre-perturbation parameters alone, which were validated using data from independent baseline time-points. Most of the parameters contributing to prediction delineated temporally-stable baseline differences in immune cell populations across individuals, raising the prospect of immune health monitoring before intervention (Tsang, Schwartzberg et al, Cell 2014). The framework we detail provides a potential resource for studying human immunity in health and disease. In the last year, we have been carrying out a similar project comparing responses to both an unadjuvented and an adjuvented vaccine against Influenza H5N1. To complement our work, we have added new functional assays and characterization of follicular T helper cells, a key T cell population that helps B cells make long-term antibody responses. We are using these approaches to look at responses to immunization in greater depth and to optimize technical and scientific approaches to better understand vaccines.