Pan-vaccine Analysis to Test the Impact of Cytomegalovirus on Vaccine Efficacy
泛疫苗分析测试巨细胞病毒对疫苗功效的影响
基本信息
- 批准号:10026284
- 负责人:
- 金额:$ 24.21万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-06-01 至 2022-05-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAffectBayesian AnalysisBayesian ModelingBayesian NetworkBiological AssayCellsCessation of lifeCommunicable DiseasesCommunitiesComputer ModelsCytomegalovirusCytomegalovirus InfectionsCytomegalovirus VaccinesCytometryDataData AnalysesData SetDatabasesDengueDiagnosisDiseaseGoalsHemagglutininHepatitisHepatitis BHerpesviridaeHeterogeneityHumanImmuneImmune responseImmune systemImmunologyIndividualInfluenzaInfluenza vaccinationJointsKnowledgeMachine LearningMalariaMalaria VaccinesMeasurementMeasuresMeta-AnalysisMethodsModelingNeutralization TestsOutcomeOutcome MeasurePathway AnalysisPopulationResearchResearch PersonnelResourcesSerologic testsShapesStandardizationStatistical ModelsTestingTimeTuberculosisUnited StatesVaccinationVaccine DesignVaccinesValidationVirusVirus LatencyWest Nile virusYellow FeverYellow Fever Vaccinecytokinedesignimprovedindividual responseinfluenza virus vaccinemalaria infectionmultimodalitynetwork modelspathogenphenotypic dataresponsetooltranscriptomicsvaccine efficacyvaccine responsevaccine trial
项目摘要
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.
项目概要/摘要
巨细胞病毒 (CMV) 感染约 50% 的美国人口。即使 CMV 以潜伏状态存在
在健康个体中,它深刻地塑造了免疫系统。最近的研究表明巨细胞病毒感染
改变对流感疫苗的免疫反应。然而,CMV 对疗效的确切影响
流感疫苗仍存在争议。此外,CMV 如何塑造针对其他疾病的免疫反应
疫苗未知。我们假设潜伏的 CMV 感染会引起免疫系统的重大变化。
系统,它改变了多种类型疫苗的功效。 ImmPort 数据库目前托管 133
疫苗研究,涵盖21种疫苗,为我们测试我们的疫苗创造了前所未有的机会
假设。我们将进行全面的荟萃分析来测试 CMV 和疫苗之间的关系
功效,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。利用团队在计算方面的专业知识
免疫学和 ImmPort 上丰富的数据集,我们将实现以下目标。目的1:测试CMV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
测试 CMV 的影响。我们将在研究中量化和标准化流感疫苗的功效,
这是通过接种疫苗前后的血凝素抑制(HAI)测定来测量的。我们还将
直接通过血清学检测或间接通过免疫检测确定受试者的 CMV 感染状态
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,同时考虑流感疫苗的
考虑研究之间的异质性。目标2:流感疫苗的贝叶斯网络分析
回复。我们将协调流感疫苗研究中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)来模拟免疫成分之间的相互作用
随着时间的推移。使用贝叶斯网络,我们将研究 CMV 塑造结果的机制
流感疫苗接种。目标3: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进行分析(例如西尼罗河疫苗、乙型肝炎疫苗、黄热病疫苗、疟疾疫苗和
结核)。我们将使用特定于疫苗类型的检测来量化疫苗功效,例如
用于疟疾疫苗的受控人类疟疾感染 (CHMI) 和斑块减少中和试验
用于黄热病疫苗。我们将进行单独的网络分析来描述关系
CMV 和个体疫苗的免疫反应之间的关系。然后我们将进行联合分析
疫苗类型以确定 CMV 对不同疫苗类型的共同影响。
项目成果
期刊论文数量(0)
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Zicheng Hu其他文献
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{{ truncateString('Zicheng Hu', 18)}}的其他基金
Pan-vaccine Analysis to Test the Impact of Cytomegalovirus on Vaccine Efficacy
泛疫苗分析测试巨细胞病毒对疫苗功效的影响
- 批准号:
10171553 - 财政年份:2020
- 资助金额:
$ 24.21万 - 项目类别:
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