Immune Responses to Influenza Vaccination

流感疫苗的免疫反应

• 批准号: 8948404
• 负责人: PAMELA SCHWARTZBERG
• 金额: $ 8.89万
• 依托单位: NATIONAL HUMAN GENOME RESEARCH INSTITUTE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8948404
• 关键词: Accounting; Affect; Age; Antibodies; Antibody Formation; Ascaridil; Autoimmune Diseases; Autoimmunity; B-Lymphocytes; Biological; Biological Markers; Blood; Blood Cells; Cells; Chronic Disease; Communicable Diseases; Computer Simulation; Data; Databases; Development; Diabetes Mellitus; Disease; Disease Progression; Ethnic Origin; Frequencies; Functional disorder; Gender; Gene Expression; Gene Expression Profile; Generations; Genome; Goals; Health; Heart Diseases; Human; Immune; Immune response; Immune system; Immunity; Immunization; Immunology; Individual; Inflammation; Influenza; Influenza vaccination; Institutes; Intervention; Intramural Research Program; Link; Malignant Neoplasms; Methods; Mining; Modality; Modeling; Molecular; Monitor; Nerve Degeneration; New York; Obesity; Pathogenesis; Pathway interactions; Plasmablast; Population; Prevention; Resources; Serologic tests; Serum; Systems Biology; Technology; Therapeutic; Time; United States National Institutes of Health; Vaccination; Vaccines; Variant; Work; clinical phenotype; computer framework; cytokine; design; improved; new therapeutic target; predictive modeling; response; sample collection

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 have 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, submitted; 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. We are now starting to use these approaches to look at responses to immunization in greater depth and to optimize technical and scientific approaches.

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被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，还包括癌症、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，我协调了NIH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异如何导致免疫应答和疾病（Tsang，Schwartzberg等人提交; Dickler，H.等人，Ann纽约科学院，2013）。 作为人类免疫建模的第一步，我们深入分析了基线和流感疫苗接种干扰的免疫参数。外周血细胞转录组，血清细胞因子，流感病毒滴度，126个细胞亚群的频率，和B细胞反应进行了评估之前和之后，在63个人的疫苗接种，并用于开发一个计算框架，剖析个体间和个体内的变化，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，我们已经能够显示与疫苗应答相关的疫苗接种后基因表达特征，而且还将这些与B细胞浆母细胞群体的扩增联系起来。重要的是，使用考虑了预先存在的血清学、年龄、种族和性别的影响的方法，我们证明了对流感疫苗接种的大部分疫苗接种后应答和预测特征受到疫苗接种前滴度的严重影响。引人注目的是，独立于年龄和预先存在的抗体滴度，我们发现可以单独使用预扰动参数构建准确的模型，并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绘了个体间免疫细胞群体的时间稳定基线差异，提高了干预前免疫健康监测的前景（Tsang，Schwartzberg et al，Cell 2014）。我们详细的框架提供了一个潜在的资源，研究人类免疫健康和疾病。我们现在开始使用这些方法来更深入地研究免疫反应，并优化技术和科学方法。