Transcriptomics to define biomarkers of neonatal vaccine immunogenicity

转录组学定义新生儿疫苗免疫原性的生物标志物

• 批准号: 10063823
• 负责人: Robert Ernest William Hancock
• 金额: $ 25.27万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-27 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10063823
• 关键词: Address; Adult; Age; Automobile Driving; Binding; Bioinformatics; Biological Markers; Biology; Blood; Blood Cells; Blood specimen; Cardiovascular system; Cattle; Cell physiology; Clinical; Clinical assessments; Collection; Complex; Confounding Factors (Epidemiology); Custom; Data; Data Set; Database Management Systems; Databases; Disease; Endocrine system; Environment; Epigenetic Process; Event; Gender; Gene Expression; Gene Proteins; Genetic; Genetic Transcription; Goals; Hand; Health; Human; Immune; Immune system; Immunization; Immunology; Individual; Infant; Innate Immune Response; Interferons; Internet; Lymphatic System; Mediator of activation protein; Meta-Analysis; Metadata; Methods; MicroRNAs; Mining; Mus; Neonatal; Nervous system structure; Network-based; Newborn Infant; Nutritional status; Pathway interactions; Patients; Physiological; Pilot Projects; Plasma; Post-Translational Protein Processing; Procedures; Process; Proteomics; Risk; STAT1 gene; Services; Signal Transduction; Stimulus; System; Systems Biology; Vaccination; Vaccines; Vision; Visual; Visualization; Whole Blood; base; cell type; demographics; experience; gene product; immunogenicity; immunological status; insight; next generation sequencing; open source; programs; prospective; protein protein interaction; remote location; response; sample collection; skills; success; tool; transcriptome sequencing; transcriptomics

Project Summary Here we propose to utilize Network Biology methods to identify transcriptomic signatures that correlate with protective responses to immunization and characterize the pathways, hubs, and key mediators associated with effective neonatal immunization. The immune system is complex and comprises 1,500 to 5,000 individual gene products, as well as epigenetic events, miRNAs, posttranslational modifications, etc. Furthermore it is well recognized that the immune system is integrated with multiple physiologic systems, and is influenced by genetics, age, nutritional status, gender, environment and underlying diseases or health. Studying individual genes, proteins, pathways and/or cell types to understand immune processes has provided us with an incomplete picture of the intricacies of the cellular processes that take place in both health and disease. Thus understanding how individuals respond to immune challenge, for example vaccination, requires a more holistic systems-level approach. We have developed a substantial collection of skill sets and tools to enable consideration of all gene expression events occurring in the blood of newborns/infants (and/or adults), and ways of bioinformatically handing these data to enable network-oriented insights while considering all factors (termed meta-data, and including demographics and differences in clinical assessments) that might act as confounding variables. In particular we will use Network Biology to understand the impact of vaccination on immune status and ultimately what factors determine the relative success of vaccination in individuals. Our Transcriptomics Service Core (SC1) will develop transcriptomic data using the next generation sequencing method of RNA-Seq. Downstream analyses will utilize our customized databases and analysis tools. InnateDB is our popular (>6 million hits) open-source database and systems biology analysis platform of all the genes, proteins, contains experimentally validated molecular interactions, and pathways in innate immune responses of humans and other species. In addition we will apply our newest tool, the NetworkAnalyst platform, which features statistical, visual and network-based approaches for meta-analysis and systems-level interpretation of transcriptomic, and proteomic data. NetworkAnalyst delivers extremely fast network layouts, hub analysis and visualization enabling unbiased examination of large transcriptomic datasets as protein-protein interaction networks. Mining of the information for subnetworks, hubs and pathways permits unique insights into data and value-added insights into differences due to experimental conditions and stimuli. Critically, we have already de-risked all procedures for this project including sample collection, transport from remote locations, RNA-Seq and downstream bioinformatic analysis and have already demonstrated our ability to develop new insights/hypotheses into the potential mechanisms driving vaccine-induced neonatal responses in our pilot studies. We anticipate that our Core will make a substantial contribution to the overall success of this project.

项目摘要 在这里，我们建议利用网络生物学方法来识别与以下相关的转录组签名： 免疫的保护性反应，并描述与免疫相关的途径，枢纽和关键介质 有效的新生儿免疫。免疫系统是复杂的，包括1,500至5,000个单独的基因 产物，以及表观遗传事件，miRNA，翻译后修饰等。 认识到免疫系统与多个生理系统相结合，并受到以下因素的影响 遗传、年龄、营养状况、性别、环境和基础疾病或健康。研究单个 基因、蛋白质、途径和/或细胞类型来理解免疫过程，为我们提供了一个 对健康和疾病中发生的细胞过程的复杂性的不完整的描述。因此 了解个体如何应对免疫挑战，例如疫苗接种，需要更全面的 系统级方法。我们已经开发了大量的技能和工具， 考虑新生儿/婴儿（和/或成人）血液中发生的所有基因表达事件，以及 生物信息学处理这些数据的方法，以实现面向网络的见解，同时考虑所有因素 （称为元数据，包括人口统计学和临床评估的差异）， 混杂变量特别是，我们将使用网络生物学来了解疫苗接种对 免疫状态以及最终决定个体疫苗接种相对成功的因素。我们 转录组学服务核心（SC 1）将使用下一代测序技术开发转录组学数据 RNA-Seq.下游分析将利用我们定制的数据库和分析工具。InnateDB 是我们流行的（超过600万点击）开源数据库和系统生物学分析平台的所有基因， 蛋白质，包含实验验证的分子相互作用，以及先天免疫反应的途径 人类和其他物种。此外，我们将应用我们最新的工具，NetworkAnalyst平台， 以统计、可视化和基于网络的方法进行荟萃分析和系统级解释， 转录组学和蛋白质组学数据。NetworkAnalyst提供极快的网络布局、集线器分析和 可视化使大型转录组数据集的无偏见检查成为蛋白质-蛋白质相互作用 网络.对子网、枢纽和路径的信息挖掘允许对数据和 由于实验条件和刺激的差异增值的见解。关键是，我们已经 降低了该项目的所有程序的风险，包括样本收集、从远程位置运输、RNA-Seq 和下游生物信息学分析，并已证明我们有能力开发新的 在我们的试点中，对驱动疫苗诱导的新生儿反应的潜在机制的见解/假设 问题研究我们预计，我们的核心将为该项目的整体成功做出重大贡献。