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 每年有超过200万婴儿死于感染，尤其是在资源贫乏的环境中非常年轻的婴儿。 此外，由于独特的免疫史，新生儿不太容易受到对大多数人的保护免疫回报 疫苗。因此，改善新生儿免疫抑制需要对疫苗有更好的生物学理解 与保护相对应的诱导免疫反应。 HIPC项目1提出创新系统 生物投资以获得对疫苗诱导的免疫力的更全面的看法。我们将使用小说高级 统计和计算方法，分析了分子和细胞的非常大而无偏的数据集 从接受免疫的新生儿获得的小血液样本测量的信息 乙型肝炎疫苗（HBV），有或没有巴西氏菌Calmette-guérin（BCG）。分子数据集将 包括数万个基因表达读出的精确测量（基因转录本[RNA] 和蛋白质）将使用最先进的方法和仪器（例如下一代）生成 测序（RNA-SEQ）和质谱法（服务核1和2，以及数据管理核心）。我们的 初步数据，来自几内亚比索和MRC-Gambia的西非队列的分析， 证明了在新生儿中测量转录组和蛋白质组学终点的可行性 外周血并建议扩大免疫（EPI）疫苗诱导的分子特征 在早期生活。预测随后的免疫原性的分子反应特征和生物标志物分类器， 将确定保护感染的保护（COP）特别相关。创新的生物信息学 数据驱动的生物标志物积分方法将揭示基因表达的模式，bionetworks， 与成功免疫相关的分子途径和生物标志物分类器和/或优化 免疫原性。我们将通过追求以下特定目标来实现我们的目标：在特定目标1中，我们将 确定人类新生儿中的血液转录组特征，该特征与有效免疫相关，使用 疫苗前后全血RNA-seq数据集在体外（体外建模HIPC项目3）和IN 体内在特定的目标2中，我们将在体外鉴定人类新生儿血浆和细胞蛋白质组学特征 并使用研究参与者和 与RNA-seq数据集匹配（特定AIM 1）；在特定的目标3中，我们将进一步发展和 使用统计和计算方法，允许在体外和体内进行整合 和蛋白质组学数据集，包括将定义细胞亚型的高端流式细胞术分析 血液（HIPC项目2）。创新的生物信息学和生物统计学将进一步完善并发现新的 与HBV COP相关的分子/细胞特征，HBV的作用机制和电势 BCG疫苗的辅助性，从而告知新生儿疫苗的未来开发。