Immune Responses to Influenza Vaccination

开发精确的模型，允许预测扰动后的生物反应，有可能增加我们对病理生理学的机械理解，并有助于开发改进的治疗方法。人类免疫系统为开发这种系统生物学方法提供了极好的背景：许多免疫细胞和分子组分很容易从血液中获得，允许在多个时间点从个体收集样本，然后进行深入的数据生成和分析。此外，越来越多的人认识到，免疫系统和炎症有助于多种疾病的发病机制。这些疾病不仅包括传统上认为涉及免疫系统的疾病，如自身免疫性疾病和感染性疾病，而且包括癌症、心脏病、糖尿病、肥胖症、神经变性和影响大部分人群的其他慢性疾病（Germain和Schwartzberg，Nat Immunol.2011）。因此，更全面和定量地了解免疫反应是如何协调的，以及识别有效与破坏性反应的预测分子和细胞参数，可能对预防和治疗各种疾病产生重大影响。 为此，我帮助协调了NIH人类免疫学中心的一项初步研究，该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异如何影响免疫反应和疾病（Tsang，Schwartzberg等人，Cell 2014; Dickler，H.等人，Ann纽约科学院，2013）。 我们现在正在进行一项后续研究，比较对H5 N1流感无佐剂和有佐剂疫苗的反应。为了补充我们的工作，我们增加了表征滤泡T辅助细胞的新检测方法，滤泡T辅助细胞是一种帮助B细胞产生长期抗体应答的关键T细胞群体。我们正在利用这些方法更深入地研究免疫反应，并优化技术和科学方法，以更好地了解疫苗。目前正在对这些数据进行生物信息学分析。

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 helped coordinate 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). We are now conducting a follow-up study comparing responses to both an unadjuvented and an adjuvented vaccine against Influenza H5N1. To complement our work, we have added new assays characterizing 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. Bioinformatic analyses of these data are in progress.