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，我帮助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，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)。 作为这些研究的后续行动，曾俊华博士的实验室应用了CITESeq技术，以发现特定的细胞对基线状态(或设定点)的贡献，这些因素有助于疫苗的应答(Kotliarov等人，Nat Med 2020)。奇怪的是，这些相同的特征可以预测某些类型LUU的严重程度。我们还在进行一项后续研究的分析，比较未加佐剂和有佐剂的H5N1流感疫苗的反应。为了补充我们的工作，我们增加了新的分析方法，包括使用Cite-Seq。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，以更好地了解是什么产生对疫苗和与佐剂相关的签名的生产性反应。

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). As a follow-up to these studies, Dr. John Tsang's laboratory has performed CITESeq technology to uncover specific cellular contributions to baseline states (or set points) that contribute to responsiveness to vaccines (Kotliarov et al Nat Med 2020). Strkingly, these same signature can predict severity of certain types of Luus. We are also conducting analyses from a follow-up study comparing responses to both an unadjuvanted and an adjuvanted vaccine against Influenza H5N1. To complement our work, we have added new assays, including the use of CITE-Seq. We are using these approaches to look at responses to immunization in greater depth and to optimize technical and scientific approaches to better understand what generates productive responses to vaccines and signatures associated with adjuvants.