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

项目摘要/摘要 巨细胞病毒(CMV)感染了大约50%的美国人口。即使巨细胞病毒以潜伏状态存在 对于健康的人来说，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，巨细胞病毒对沙眼病毒疗效的确切影响 流感疫苗仍然存在争议。此外，CMV如何塑造对其他病毒的免疫反应 疫苗是未知的。我们假设潜伏的巨细胞病毒感染会引起免疫系统的关键变化。 系统，它改变了多种类型疫苗的效力。ImmPort数据库目前拥有133个 疫苗研究，涵盖21种疫苗，为我们创造了前所未有的机会来测试我们的 假设。我们将进行全面的荟萃分析，以检验CMV和疫苗之间的关系 效率，并将使用最先进的统计模型(例如动态贝叶斯网络)来识别 巨细胞病毒改变疫苗反应的机制。利用该小组在计算方面的专业知识 免疫学和ImmPort上丰富的数据集，我们将解决以下目标。目的1：测试巨细胞病毒的效果 关于流感疫苗的结果。我们将对ImmPort上提供的60项流感研究进行荟萃分析 以测试CMV的影响。我们将通过研究量化和标准化流感疫苗的效力， 接种前后用血凝素抑制试验(HAI)检测。我们还将 直接从血清学试验或间接从免疫学试验确定受试者的CMV感染状态。 使用尖端机器学习工具对数据进行表型分析。然后我们将测试CMV是否会增加响应 通过在统一的统计框架中分析所有研究的数据，同时采用 考虑到研究之间的异质性。AIM2：流感疫苗的贝叶斯网络分析 回应。我们将统一流感疫苗研究中的多模式免疫表型数据， 包括转录组数据、细胞学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)来对免疫组件之间的相互作用进行建模 随着时间的推移。利用贝叶斯网络，我们将研究CMV塑造结果的机制 接种流感疫苗。目的：探讨巨细胞病毒感染对其他疫苗的影响。我们将延长我们的 对流感疫苗以外的疫苗的分析(例如西尼罗河、乙肝、黄热病、疟疾和 结核病)。我们将使用针对疫苗类型的分析方法来量化疫苗效果，例如 疟疾疫苗的受控人类疟疾感染和空斑减少中和试验 为了黄热病疫苗。我们将执行单独的网络分析以确定关系的特征 CMV和单个疫苗的免疫反应之间的关系。然后，我们将对以下对象进行联合分析 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.