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: 9245973
• 负责人: Robert Ernest William Hancock
• 金额: $ 22.59万
• 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 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9245973
• 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; Databases; Disease; Endocrine system; Environment; Epigenetic Process; Event; Gender; Gene Expression; Gene Proteins; Genetic; Genetic Transcription; Goals; Hand; Health; Human; Imagery; 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; 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 对免疫的保护性反应，并表征与之相关的途径，枢纽和关键介体 有效的新生儿免疫。免疫系统很复杂，包括1,500至5,000个单独的基因 产品以及表观遗传事件，miRNA，翻译后修饰等。此外，它很好 认识到免疫系统与多个生理系统集成在一起，并受 遗传学，年龄，营养状况，性别，环境和潜在疾病或健康。研究个人 了解免疫过程的基因，蛋白质，途径和/或细胞类型为我们提供了 在健康和疾病中发生的细胞过程的复杂性不完整。因此 了解个人如何应对免疫挑战，例如疫苗接种，需要更全面 系统级方法。我们已经开发了大量技能和工具来启用 考虑所有基因表达事件发生在新生儿/婴儿（和/或成人）的血液中，以及 生物信息传递这些数据以实现面向网络的见解的方式，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） 混淆变量。特别是我们将使用网络生物学了解疫苗接种的影响 免疫状态，最终是哪些因素决定了个体疫苗接种的相对成功。我们的 转录组学服务核心（SC1）将使用下一代测序开发转录组数据 RNA-Seq的方法。下游分析将利用我们的自定义数据库和分析工具。 InnatedB 是我们流行的（> 600万击中）的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，以及先天免疫反应中的途径 人类和其他物种。此外，我们将应用我们的最新工具，网络分析平台， 具有统计，基于视觉和网络的方法，用于荟萃分析和系统级别的解释 转录组和蛋白质组学数据。 NetworkAnalyst提供非常快速的网络布局，集线器分析和 可视化，可以无偏见的大型转录组数据集作为蛋白质 - 蛋白质相互作用 网络。挖掘子网，集线器和途径的信息允许对数据的独特见解和 由于实验条件和刺激而引起的差异的增值见解。至关重要的是，我们已经 该项目的所有程序都降低了风险，包括样本收集，从偏远位置运输，RNA-seq 和下游生物信息学分析，并且已经证明了我们开发新的能力 洞察力/假设对驱动疫苗诱导的新生儿反应的潜在机制 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。