Elucidation of the transcriptional network underlying the Th17 lineage program.

阐明 Th17 谱系程序背后的转录网络。

• 批准号: 8047317
• 负责人: RICHARD A BONNEAU
• 金额: $ 473.11万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2013-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8047317
• 关键词: Address; Algorithms; Animals; Area; Attenuated; Autoimmune Diseases; Autoimmunity; Binding; Binding Sites; Biology; Blood; CD4 Positive T Lymphocytes; Candida albicans; Cell Differentiation process; Cell physiology; Cells; Chromatin; Citrobacter rodentium; Code; Complement; Complex; Computing Methodologies; Coupled; Crohn' s disease; DNA Methylation; DNA Polymerase II; Data; Data Analyses; Data Set; Dioxins; Disease; Disease model; EP300 gene; Elements; Epigenetic Process; Event; Factor Analysis; Functional RNA; Gene Expression Profile; Gene Targeting; Genes; Genetic; Genome; Genomics; Helper-Inducer T-Lymphocyte; Human; In Vitro; Infection; Inflammation; Inflammation Mediators; Inflammatory; Interleukin-17; Interleukin-6; Intestines; Klebsiella pneumonia bacterium; Learning; Lung; Maps; Measures; Methodology; Methyltransferase; Microbe; Modeling; Molecular; Molecular Profiling; Mucosal Immunity; Mus; Mycoses; Nodal; Nuclear Orphan Receptor; Organ; Pathway Analysis; Pathway interactions; Phenotype; Process; Psoriasis; RNA; RNA Sequence Analysis; Regulation; Regulator Genes; Regulatory T-Lymphocyte; Relative (related person); Rheumatoid Arthritis; Role; Sampling; Small RNA; Sorting - Cell Movement; Specific qualifier value; Surface; System; T-Lymphocyte; Technology; Testing; Therapeutic; Time; Time Series Analysis; Umbilical Cord Blood; Work; antimicrobial; base; chromatin immunoprecipitation; cytokine; design; experience; genome-wide; high throughput technology; histone acetyltransferase; histone modification; human disease; in vivo; in vivo Model; interleukin-22; knock-down; member; mouse model; network models; new therapeutic target; next generation; novel; pathogen; peripheral blood; polarized cell; programs; public health relevance; research study; skills; small hairpin RNA; small molecule; success; therapeutic target; tool; transcription factor

DESCRIPTION (provided by applicant): This application addresses the broad Thematic Area 1, Applying Genomics and Other High Throughput Technologies, to study the transcriptional regulatory network that governs the differentiation of Th17 cells. Following infection with different microbes, CD4+ T lymphocytes differentiate into T helper (Th) cells with diverse effector functions to contain the offending agent. Th17 cells, which produce the cytokines IL-17, IL-17F, and IL-22, are important for clearing mucosal pathogens, but are also major contributors to inflammation and autoimmune disease. Multiple autoimmunity models in mice require Th17 cells, and there is accumulating evidence that these cells have key roles in human diseases such as Crohn's disease, psoriasis, and rheumatoid arthritis. We demonstrated that the orphan nuclear receptor ROR?t is required for the differentiation of Th17 cells and is also sufficient to induce activated T cells to acquire a Th17 phenotype, highlighting it as a critical lineage-defining transcription factor (TF). Loss of ROR?t or its inhibition with small molecules abrogates autoimmune disease in mouse models, suggesting that it is a good candidate therapeutic target for human inflammatory diseases. ROR?t is embedded in a complex network that includes several other TFs that are essential for, or contribute to, differentiation and function of Th17 cells. These include Stat-3, IRF-4, Ahr, BATF, Runx1/CBF¿, c-Maf, and RORa. ROR?t is also often co-expressed with the regulatory T (Treg) cell- specifying factor Foxp3, which restrains Th17 cell differentiation. We propose to perform a comprehensive genomic analysis of the Th17 transcriptional program in order to learn the regulatory network controlling Th17 cell differentiation. We will do this by using next-generation sequencing technologies to identify target gene occupancy by these relevant transcription factors (measured by ChIP-seq), associated epigenetic changes, and corresponding expression of coding and non-coding RNAs associated with Th17 cell differentiation. Our computational methods, designed specifically for analysis of time series data, will be applied to samples at multiple time points from wild type and TF-deficient T cells and will provide information for characterizing functional cis-regulatory modules, assessing TF cooperatively, and building iterative network models that will identify new critical nodes involved in functional differentiation of these inflammatory cells. Analysis of Th17 cells isolated directly from mice undergoing diverse inflammatory processes and from human blood will be incorporated to test network predictions. We have assembled a team whose members have the complementary skills needed for the success of this project: a group that has made fundamental contributions to the biology of Th17 cells; a genome center with extensive experience in high throughput sequencing and data analysis; and a computational group that has developed advanced algorithms for inferring transcriptional networks and predicting functional nodes. We anticipate that the proposed studies will help identify new targets for therapeutic modulation of Th17 cells in humans to either boost mucosal immunity or attenuate inflammation. PUBLIC HEALTH RELEVANCE: We will use a combination of whole-genome methodologies, including ChIP-seq and RNA-seq, to characterize the transcriptional network of Th17 cells that have key roles in mucosal immunity and in autoimmune diseases. Functional studies based on predictions of the network analysis will uncover novel targets for therapeutic approaches to selectively enhance or attenuate Th17 cell function.

描述（由申请人提供）：本申请涉及广泛的主题领域1，应用基因组学和其他高通量技术，以研究控制Th 17细胞分化的转录调控网络。在感染不同的微生物后，CD 4 + T淋巴细胞分化为具有不同效应功能的T辅助细胞（Th），以包含攻击性因子。产生细胞因子IL-17、IL-17 F和IL-22的Th 17细胞对于清除粘膜病原体很重要，但也是炎症和自身免疫性疾病的主要贡献者。小鼠中的多种自身免疫模型需要Th 17细胞，并且有越来越多的证据表明这些细胞在人类疾病如克罗恩病、银屑病和类风湿性关节炎中起关键作用。我们证明，孤儿核受体ROR？t是Th 17细胞分化所需的，也足以诱导活化的T细胞获得Th 17表型，突出了它作为一个关键的谱系定义转录因子（TF）。失去ROR？在小鼠模型中，T或其小分子抑制消除了自身免疫性疾病，这表明它是人类炎症性疾病的良好候选治疗靶点。吼？T细胞嵌入一个复杂的网络中，该网络包括几个其他TF，这些TF对于Th 17细胞的分化和功能至关重要或有贡献。其中包括Stat-3、IRF-4、Ahr、BATF、Runx 1/CBF？、c-Maf和RORa。吼？T细胞还经常与调节性T（Treg）细胞特异性因子Foxp 3共表达，Foxp 3抑制Th 17细胞分化。我们建议进行全面的基因组分析的Th 17转录程序，以了解控制Th 17细胞分化的调控网络。我们将通过使用下一代测序技术来确定这些相关转录因子的靶基因占用率（通过ChIP-seq测量），相关的表观遗传变化以及与Th 17细胞分化相关的编码和非编码RNA的相应表达。我们的计算方法专为分析时间序列数据而设计，将应用于来自野生型和TF缺陷T细胞的多个时间点的样本，并将为表征功能顺式调节模块、合作评估TF和构建迭代网络模型提供信息，这些模型将识别参与这些炎症细胞功能分化的新关键节点。直接从经历不同炎症过程的小鼠和人类血液中分离的Th 17细胞的分析将被纳入测试网络预测。我们组建了一个团队，其成员具有成功完成该项目所需的互补技能：一个对Th 17细胞生物学做出了根本性贡献的团队;一个在高通量测序和数据分析方面拥有丰富经验的基因组中心;以及一个开发了用于推断转录网络和预测功能节点的先进算法的计算团队。我们预计，拟议的研究将有助于确定治疗调节人体Th 17细胞的新靶点，以增强粘膜免疫力或减轻炎症。 公共卫生关系：我们将使用全基因组方法的组合，包括ChIP-seq和RNA-seq，来表征在粘膜免疫和自身免疫性疾病中起关键作用的Th 17细胞的转录网络。基于网络分析预测的功能研究将为选择性增强或减弱Th 17细胞功能的治疗方法揭示新的靶点。