Innovative OMIC integration to predict immunogenicity

创新的 OMIC 整合预测免疫原性

• 批准号: 10063825
• 负责人: Scott Tebbutt
• 金额: $ 30.82万
• 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/10063825
• 关键词: Achievement; Adjuvanticity; Affect; African; Age; BCG Live; BCG Vaccine; Bioinformatics; Biological; Biological Assay; Biological Markers; Biometry; Blood; Blood Cells; Blood specimen; Cells; Clinical; Coupled; Data; Data Set; Decision Trees; Development; Discriminant Analysis; Environment; Enzyme-Linked Immunosorbent Assay; Epidemiology; Flow Cytometry; Future; Gambia; Gene Expression; Gene Expression Profile; Genes; Genetic Transcription; Goals; Guinea-Bissau; Hepatitis B Vaccines; Human; Immune; Immune response; Immunity; Immunization; Immunology; In Vitro; Industrialization; Infant; Infection; Investigation; Leukocytes; Life; Mass Spectrum Analysis; Measurement; Measures; Mediating; Methods; Modeling; Molecular; Molecular Profiling; Molecular Target; Neonatal; Newborn Infant; Papua New Guinea; Participant; Pathway interactions; Pattern; Plasma; Proteins; Proteomics; RNA; Resources; Sampling; Services; Site; Systems Biology; Time; Transcript; Universities; Vaccines; Western Australia; Whole Blood; analytical tool; base; bioinformatics network; biological systems; biomarker development; biomarker-driven; cohort; data management; high risk; human model; immunogenicity; in vitro Model; in vivo; innovation; instrument; mortality; multiple omics; next generation sequencing; novel; peripheral blood; predictive marker; predictive signature; programs; proteomic signature; response; sex; transcriptome sequencing; transcriptomics; vaccine-induced immunity

PROJECT SUMMARY More than 2 million infants die every year from infections, particularly the very young in resource-poor settings. Moreover, due to distinct immunity, newborns are less apt to mount protective immune responses to most vaccines. Improvement of newborn immunization thus requires a better biological understanding of vaccine- induced immune responses that correspond to protection. HIPC Project 1 proposes an innovative systems biological investigation to gain a more holistic view of vaccine-induced immunity. We will use novel advanced statistical and computational approaches to analyze very large and unbiased datasets of molecular and cellular information measured from small samples of blood obtained from newborns undergoing immunization with hepatitis B vaccine (HBV), given with or without Bacille Calmette-Guérin (BCG). The molecular datasets will consist of precise measurements of tens of thousands of gene expression read-outs (gene transcripts [RNA] and proteins) that will be generated using state-of-the-art methods and instruments, such as next generation sequencing (RNA-Seq) and mass spectrometry (Service Cores 1 & 2, and Data Management Core). Our preliminary data, derived from analyses of West African cohorts in Guinea Bissau and MRC-Gambia, demonstrate feasibility of measuring transcriptomic and proteomic endpoints in small volumes of newborn peripheral blood and suggest Expanded Program on Immunization (EPI) vaccine-induced molecular signatures in early life. Molecular response signatures and biomarker classifiers that predict subsequent immunogenicity, especially correlates of protection (CoP) against infection, will be identified. Innovative bioinformatics-based and data-driven biomarker integration approaches will reveal patterns of gene expression, bionetworks, molecular pathways and biomarker classifiers associated with successful immunization, and/or sub-optimal immunogenicity. We will achieve our goal by pursuing the following Specific Aims: in Specific Aim 1, we will identify blood transcriptomic signatures in human newborns that correlate with effective immunization, using pre- and post-vaccine whole blood RNA-Seq datasets both in vitro (in vitro modeling HIPC Project 3) and in vivo; in Specific Aim 2, we will identify human newborn blood plasma and cellular proteomic signatures in vitro and in vivo that correlate with effective immunization, using proteomic datasets that are study participant- and time-matched to the RNA-Seq datasets (Specific Aim 1); and in Specific Aim 3, we will further develop and use statistical and computational approaches to allow integration across the in vitro and in vivo transcriptomic and proteomic datasets, including with the high-end flow cytometry analyses that will define cellular subtypes in blood (HIPC Project 2). Innovative bioinformatics and biostatistics will further refine and discover new molecular/cellular signatures associated with HBV CoP, mechanisms of action of HBV and potential adjuvanticity of BCG vaccine, thereby informing future development of neonatal vaccines.

项目摘要 每年有200多万婴儿死于感染，特别是在资源贫乏的环境中的幼儿。 此外，由于独特的免疫力，新生儿不太容易对大多数疾病产生保护性免疫反应。 疫苗。因此，改善新生儿免疫需要对疫苗有更好的生物学理解- 诱导相应的免疫反应。重债穷国项目1提出了一种创新的制度， 生物学调查，以获得疫苗诱导免疫的更全面的看法。我们将使用先进的 统计和计算方法来分析分子和细胞的非常大的和无偏的数据集 从接受免疫接种的新生儿获得的少量血液样本中测量的信息 B型肝炎疫苗（HBV），与或不与卡介苗（BCG）一起接种。分子数据集将 包括精确测量成千上万的基因表达读出（基因转录[RNA]） 和蛋白质），将使用最先进的方法和仪器，如下一代 测序（RNA-Seq）和质谱（服务核心1和2，以及数据管理核心）。我们 初步数据来自对几内亚比绍和刚果民主共和国冈比亚的西非队列的分析， 证明在小体积新生儿中测量转录组学和蛋白质组学终点的可行性 外周血和建议扩大免疫计划（EPI）疫苗诱导的分子特征 在早期生活中。预测后续免疫原性的分子应答特征和生物标志物分类器， 尤其是抗感染保护相关因子（CoP）。基于创新生物信息学 数据驱动的生物标志物整合方法将揭示基因表达模式，生物网络， 与成功免疫相关的分子途径和生物标志物分类器，和/或次优 免疫原性我们将通过以下具体目标实现我们的目标：在具体目标1中，我们将 鉴定人类新生儿中与有效免疫相关的血液转录组学特征， 体外（体外建模HIPC项目3）和体内疫苗接种前和接种后全血RNA-Seq数据集 在特定目标2中，我们将在体外鉴定人新生儿血浆和细胞蛋白质组特征 和体内与有效免疫相关的蛋白质组学数据集， 与RNA-Seq数据集时间匹配（具体目标1）;在具体目标3中，我们将进一步开发和 使用统计和计算方法，以允许整合体外和体内转录组学 和蛋白质组学数据集，包括高端流式细胞术分析， 血液（重债穷国项目2）。创新的生物信息学和生物统计学将进一步完善和发现新的 与HBV CoP相关的分子/细胞特征，HBV的作用机制和潜在的 BCG疫苗的佐剂性，从而为新生儿疫苗的未来发展提供信息。