Elucidation of the transcriptional network underlying the Th17 lineage program.

描述（由申请人提供）：该申请涉及广泛的主题区域1，应用基因组学和其他高通量技术，研究控制Th17细胞分化的转录调控网络。在感染不同的微生物后，CD4+ T淋巴细胞分化为具有不同效应功能的T辅助细胞（Th），以遏制病原体。Th17细胞产生细胞因子IL-17、IL-17F和IL-22，对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。小鼠的多种自身免疫模型需要Th17细胞，越来越多的证据表明，这些细胞在克罗恩病、牛皮癣和类风湿性关节炎等人类疾病中起着关键作用。我们证明了孤儿核受体ROR？t是Th17细胞分化所必需的，也足以诱导活化的t细胞获得Th17表型，突出表明它是一个关键的谱系定义转录因子（TF）。丧失ROR？在小鼠模型中，T或其小分子抑制可消除自身免疫性疾病，这表明它是人类炎症性疾病的良好候选治疗靶点。ROR吗?t嵌入在一个复杂的网络中，该网络包括其他几个对Th17细胞的分化和功能至关重要或有贡献的TFs。这些包括Stat-3， IRF-4, Ahr， BATF, Runx1/CBF¿，c-Maf和RORa。ROR吗?t也经常与抑制Th17细胞分化的调节性t （Treg）细胞特异性因子Foxp3共表达。我们建议对Th17转录程序进行全面的基因组分析，以了解控制Th17细胞分化的调控网络。我们将使用下一代测序技术来确定这些相关转录因子（通过ChIP-seq测量）、相关表观遗传变化以及与Th17细胞分化相关的编码和非编码rna的相应表达的目标基因占用。我们的计算方法是专门为分析时间序列数据而设计的，将应用于野生型和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、协同评估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Th17细胞的分析将被纳入测试网络预测。我们已经组建了一个团队，其成员拥有该项目成功所需的互补技能：一个对Th17细胞的生物学做出了根本性贡献的团队；在高通量测序和数据分析方面具有丰富经验的基因组中心；还有一个计算小组，他们开发了先进的算法来推断转录网络和预测功能节点。我们预计，拟议的研究将有助于确定人类治疗性调节Th17细胞的新靶点，以增强粘膜免疫或减轻炎症。

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.