Pan-vaccine Analysis to Test the Impact of Cytomegalovirus on Vaccine Efficacy

项目总结/摘要 巨细胞病毒（CMV）感染约50%的美国人口。即使巨细胞病毒以潜伏状态存在 在健康的个体中，它深刻地塑造了免疫系统。最近的研究表明，CMV感染 改变对流感疫苗的免疫反应。然而，CMV对治疗效果的确切影响 流感疫苗仍有争议。此外，CMV如何塑造对其他免疫系统的免疫反应， 疫苗未知。我们假设潜伏的CMV感染诱导免疫系统的关键变化， 系统，它改变了多种类型的疫苗的效力。ImmPort数据库目前拥有133个 疫苗研究，涵盖21种疫苗，为我们提供了前所未有的机会来测试我们的 假说.我们将进行一项全面的荟萃分析，以测试CMV和疫苗之间的关系 有效性，并将使用最先进的统计模型（例如，动态贝叶斯网络）来识别 CMV改变疫苗反应的机制。利用该组织在计算方面的专业知识 免疫学和丰富的数据集在ImmPort，我们将致力于以下目标。目的1：测试CMV的效果 流感疫苗的效果。我们将对ImmPort上提供的60项流感研究进行荟萃分析 来测试巨细胞病毒的影响我们将量化和标准化流感疫苗在不同研究中的有效性， 其在接种疫苗之前和之后通过血凝素抑制（HAI）测定来测量。我们还将 确定受试者的CMV感染状态，直接从血清学试验或间接从免疫- 使用尖端的机器学习工具对数据进行表型分析。然后我们将测试CMV是否会增加反应， 通过在统一的统计框架中分析所有研究的数据， 研究之间的异质性。目的2：流感疫苗的贝叶斯网络分析 反应我们将协调来自流感疫苗研究的多模式免疫表型数据， 包括转录组学数据、细胞计数数据和细胞因子测量。我们将使用最先进的网络 分析方法（例如，动态贝叶斯网络）来模拟免疫组件之间的相互作用 随着时间使用贝叶斯网络，我们将研究CMV塑造结果的机制 流感疫苗。目的3：探讨CMV感染对其他疫苗的影响。我们将扩大我们的 对流感疫苗以外的疫苗的分析，（例如，西尼罗河病毒、B型肝炎、黄热病、疟疾和 肺结核）。我们将使用特定于疫苗类型的测定来量化疫苗效力，例如 疟疾疫苗和空斑减少中和试验的控制人类疟疾感染（CHMI） 黄热病疫苗我们将执行单独的网络分析来描述这种关系 CMV和单个疫苗的免疫反应之间的关系。然后，我们将对所有数据进行联合分析。 疫苗类型，以确定CMV在疫苗类型中的共同影响。

PROJECT SUMMARY/ABSTRACT Cytomegalovirus (CMV) infects around 50% of the US population. Even though the CMV exists in a latent state in healthy individuals, it profoundly shapes the immune system. Recent studies suggest that the CMV infection alters the immune response to influenza vaccine. However, the exact effect of CMV on the efficacy of the influenza vaccine remains controversial. In addition, how CMV shapes the immune responses toward other vaccines are unknown. We hypothesize that latent CMV infection induces critical changes in the immune system, which alters the efficacy of multiple types of vaccines. The ImmPort database currently hosts 133 vaccine studies, covering 21 types of vaccines, creating an unprecedented opportunity for us to test our hypothesis. We will perform a comprehensive meta-analysis to test the relationship between CMV and vaccine efficacies, and will use state-of-art statistical models (e.g., Dynamic Bayesian Network) to identify the mechanism by which CMV alters the vaccine response. Leveraging the group's expertise in computational immunology and rich datasets on ImmPort, we will address the following aims. Aim1: Test the effect of CMV on influenza vaccine outcome. We will perform a meta-analysis of 60 influenza studies available on ImmPort to test the impact of CMV. We will quantify and standardize the efficacy of influenza vaccine across studies, which are measured by hemagglutinin inhibition (HAI) assays before and after the vaccination. We will also determine the CMV infection status in subjects, either directly from serological tests or indirectly from immune- phenotyping data using cutting-edge machine learning tools. We will then test if CMV increases the response to influenza vaccine by analyzing data from all studies in a unified statistical framework while taking the heterogeneity between studies into account. Aim2: Bayesian network analysis of influenza vaccine response. We will harmonize multimodal immune-phenotyping data from the influenza vaccine studies, including transcriptomics data, cytometry data, and cytokine measurements. We will use state-of-art network analysis methods (e.g., Dynamic Bayesian network) to model the interplay between the immune components over time. Using the Bayesian network, we will investigate the mechanism by which CMV shapes the outcome of influenza vaccination. Aim3: Explore the effect of CMV infection on other vaccines. We will extend our analysis to vaccines other than influenza vaccine, (e.g., West Nile, Hepatitis B, yellow fever, malaria, and Tuberculosis). We will quantify the vaccine efficacy using assays specific to the vaccine type, such as Controlled Human Malaria Infection (CHMI) for the malaria vaccine and Plaque Reduction Neutralization Test for the yellow fever vaccine. We will perform separate network analyses to characterize the relationship between CMV and the immune response of individual vaccines. We will then perform joint analysis across vaccine types to identify the common impact of CMV across vaccine types.