Gene Expression and Regulatory Networks in Human Leukocytes

人类白细胞的基因表达和调控网络

• 批准号: 7945283
• 负责人: CHRISTOPHE O. BENOIST
• 金额: $ 320.32万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7945283
• 关键词: Adverse event; Affect; African American; Age; Aging; Architecture; Area; Asians; Benchmarking; Biological Markers; Blood; Blood Cells; CD4 Positive T Lymphocytes; Cell Lineage; Cells; Cereals; Chromosome Mapping; Clinical Trials; Cluster Analysis; Communities; Complement; Complex; Computational Biology; Computational algorithm; Computer Analysis; DNA; DNA Microarray Chip; Data; Data Analyses; Data Set; Dendritic Cells; Diagnostic; Disease; Disease Pathway; Dissection; Engineering; Environment; European; Exons; Flow Cytometry; Functional RNA; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genetic; Genetic Structures; Genetic Variation; Genome; Genomics; Genotype; Hand; Health; Hospitals; Human; Human Genetics; Human Genome; Immune; Immunologist; Immunophenotyping; Individual; Inflammatory; Institutes; International; Internet; Knowledge; Leukocytes; Ligands; Lymphocyte; Maps; Messenger RNA; Metadata; MicroRNAs; Molecular Profiling; Monitor; Mus; Myeloid Cells; Pathogenesis; Pathway interactions; Pattern; Phenotype; Population; Predisposition; Procedures; Protocols documentation; RNA; RNA Splicing; Resolution; Resources; Sampling; T-Lymphocyte; Techniques; Technology; Time; Transcript; Treatment Efficacy; Variant; Woman; Work; base; cell type; cohort; cost; cytokine; density; genome-wide; healthy volunteer; heuristics; interdisciplinary collaboration; monocyte; prognostic; reconstruction; repository; research study; response; sample collection; tool; volunteer; young adult

DESCRIPTION (provided by applicant): Profiling of gene expression with microarrays holds great potential for human health, for illuminating disease pathways or providing biomarkers to monitor disease or its resolution. Using high-throughput approaches for genotyping, immunophenotyping and gene expression analysis, the project will examine the basis for the control of gene expression in human immune cells, and how it is influenced by natural genetic variation or aging. In practice, the project will combine several cross-informative approaches: 1) Building on the established sample/data pipelines and robust protocols of the Immunological Genome (ImmGen) project, and on established cohorts of ethnically diverse healthy volunteers at hand, microarray techniques will be used to generate whole-genome expression profiles from purified na¿ve CD4+ lymphocytes and monocytes from 600 healthy volunteers. A dense genetic map will be established for all donors. The results will elucidate how variation in the human genome affects the expression of immune genes, of key importance in understanding gene variants that bring susceptibility to immune or inflammatory disease. Computational analysis of these rich data will allow the reconstruction of regulatory connections between genes, helping to establish general modules and those specific of a given immune cell type. These data will be complemented by an orthogonal datagroup, generated from a restricted subset of 10 individuals, in which we will profile a larger set of 28 carefully delineated cell populations that exist in human blood. This work will also benefit from powerful interspecies comparison with similar experiments being performed in mice by ImmGen. 2) In addition, RNA from the same set of 28 defined cell populations will be probed with microarrays that explore other aspects of the transcriptome: i) microRNAs and other non-coding RNAs; ii) exon or splice junction arrays that will establish a map of differential splicing in human blood leukocyte. 3) The composition and reactivity of blood cells from the same donors will be established at the time of sample collection using high-throughput flow cytometry, correlating immune phenotypes with gene expression and genetic variation. 4) Genetic variability conditions the baseline levels of gene expression, but also the responsiveness to activating challenges. With samples from the same donors, Nanostring technology for transcript counting will be used to analyze the transcriptional response of defined gene sets, representing response signatures of T or dendritic cells, for a fine-grained dissection of responses to different triggers (different bacterial ligands for dendritic cells, different cytokine environment for T cells). In keeping with the resource aspect of this project, all data and interpretations will be made publicly available rapidly upon curation, allowing public querying and browsing of the data, by using and evolving the existing web architectures of the ImmGen project, of the Broad Institute, and of international repositories. RELEVANCE: Exploration with DNA chips of the genome's expression microarrays holds great potential for human health, to better understand disease and to serve as diagnostic tools. Using a combination of high-throughput genomic techniques and computational biology, we will perform a broad exploration of gene expression in human blood cells across groups of African-American, Asian and European ancestry, asking how these profiles are affected by genetic variation or by age. These results will provide an invaluable reference benchmark for the interpretation of genetic and immunological studies.

描述（由申请人提供）：用微阵列分析基因表达对人类健康、阐明疾病途径或提供生物标志物以监测疾病或其解决方案具有巨大潜力。利用高通量方法进行基因分型、免疫表型和基因表达分析，该项目将研究人类免疫细胞中基因表达控制的基础，以及它如何受到自然遗传变异或衰老的影响。在实践中，该项目将结合联合收割机的几个交叉信息的方法： 1)基于免疫基因组（ImmGen）项目的已建立的样本/数据管道和稳健的方案，以及现有的种族多样性健康志愿者的已建立队列，微阵列技术将用于从600名健康志愿者的纯化的幼稚CD 4+淋巴细胞和单核细胞生成全基因组表达谱。将为所有捐赠者建立密集的基因图谱。这些结果将阐明人类基因组的变异如何影响免疫基因的表达，这对于理解导致免疫或炎症疾病易感性的基因变异至关重要。对这些丰富数据的计算分析将允许重建基因之间的调控连接，帮助建立通用模块和特定免疫细胞类型的特定模块。这些数据将由一个正交数据组补充，该数据组由10个个体的有限子集生成，其中我们将分析存在于人类血液中的28个仔细描绘的细胞群的更大集合。这项工作也将受益于与ImmGen在小鼠中进行的类似实验进行的强大的种间比较。 2)此外，将使用微阵列探测来自同一组28个确定的细胞群的RNA，该微阵列探索转录组的其他方面：i）微小RNA和其他非编码RNA; ii）外显子或剪接点阵列，其将建立人血白细胞中差异剪接的图谱。 3)将在样本采集时使用高通量流式细胞术确定来自相同供体的血细胞的组成和反应性，将免疫表型与基因表达和遗传变异相关联。 4)遗传变异性决定了基因表达的基线水平，也决定了对激活挑战的反应。对于来自相同供体的样本，用于转录本计数的Nanostring技术将用于分析定义的基因集的转录反应，代表T或树突细胞的反应特征，用于对不同触发物（树突细胞的不同细菌配体，T细胞的不同细胞因子环境）的反应进行细粒度解剖。 为了与本项目的资源方面保持一致，所有数据和解释都将公开 通过使用和发展ImmGen项目、布罗德研究所和国际知识库的现有网络架构，在策展时迅速可用，允许公众查询和浏览数据。 相关性：利用基因组表达微阵列的DNA芯片进行探索，对人类健康、更好地了解疾病和作为诊断工具具有巨大潜力。使用高通量基因组技术和计算生物学的结合，我们将对非裔美国人，亚洲人和欧洲人血统的人类血细胞中的基因表达进行广泛的探索，询问这些基因谱如何受到遗传变异或年龄的影响。这些结果将为遗传学和免疫学研究的解释提供宝贵的参考基准。