Signaling Networks of Nuclear Receptor Transcriptional Crosstalk in Lung Cancer

肺癌核受体转录串扰的信号网络

• 批准号: 8898227
• 负责人: JUN QIN
• 金额: $ 30.47万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8898227
• 关键词: Accounting; Address; Affinity; Agonist; Bioinformatics; Biological; Biological Assay; Biology; Cell Line; Cells; Communities; Cyclic AMP; DNA; DNA Binding; Data; Data Analyses; Data Quality; Data Set; Disease; Drug Targeting; Estrogen Receptor alpha; Event; FDA approved; Family; Gefitinib; Genome; Goals; Gold; Guilt; Health; Hela Cells; HepG2; Human; Informatics; Internet; Investigation; Learning; Ligands; Link; Malignant Epithelial Cell; Malignant neoplasm of lung; Methodology; Methods; Mining; NR4A1 gene; Non-Small-Cell Lung Carcinoma; Nuclear Receptors; Oncogenic; Outcome; PPAR gamma; Pathology; Pathway interactions; Pattern; Pharmacologic Substance; Pharmacology; Play; Protein Family; Proteins; Proto-Oncogene Proteins c-akt; Research; Research Peer Review; Research Personnel; Resistance; Response Elements; Rest; Role; Signal Pathway; Signal Transduction; Solutions; Techniques; Tissues; Transcriptional Regulation; Translating; Tyrosine Kinase Inhibitor; Work; base; cancer type; data mining; drug development; human FRAP1 protein; interest; member; programs; response; small molecule; targeted treatment; tool; transcription factor; user-friendly

DESCRIPTION: Nuclear receptors have been proven to be successful as a druggable family of cellular regulators. We learned much of NR biology and pharmacology from very few NRs and their agonist/antagonist ligands. Among these well-studied NRs are ERα, GR, PR, AR, and - more recently - PPARγ. The first four NRs mentioned account for the vast majority of NR-related peer reviewed research and essentially half of FDA-approved NR targeting therapies. There are 48 members of the NR protein family in humans, and much remains to be learned for the rest of the NRs. There are at least two ways to find and prioritize pharmaceutical opportunities for the NR class. One is to find NRs that work synergistically with or regulated by the well-studied NRs and TFs - this constitutes discovery and characterization of transcriptional crosstalk between transcriptional regulators, implying "guilt of association" functions for interactors of known disease targets. The other way is to identify NRs that are activated by the major cellular signaling pathways, or NRs "effectors" of a signaling transduction cascades. To enrich our understanding of the druggable genome, we propose to gather data about dynamics of NR activation and NR crosstalk with other transcription factors (TFs) under different signaling events. Our first major goal (Aim 1) is to learn from NRs with known roles, but not fully characterized mechanisms, in disease. We will use transcription factor response element pulldown (catTFRE) - a method that we recently developed for direct profiling of TF DNA binding activity to their cognate DNA response elements - to find transcriptional effectors (NRs, their coregulators, and other interacting transcription factors) of cellular response to known small molecule modulators of better-studied NRs. A complementary approach is to use catTFRE technique to find NRs, TFs, and coregulators that are activated by 7 major signaling pathways (Aim 2). This study will link signaling events with NR activation and close substantial gaps in understanding of global integrative transcriptional impact of signaling pathways. Aim 2 data will be gathered in the context of lung cancer pathology. Aim 3 develops informatics solutions to address issues in technical and biomedical analysis of data acquired from Aims 1 and 2. Importantly, we will develop user-friendly applications and a web portal for representation and sharing of findings from this proposal.

描述：核受体已被证明是可吸毒的细胞调节剂家族。我们从很少的NR及其激动剂/拮抗剂配体中学到了NR生物学和药理学的大部分。在这些经过良好研究的NR中，有ERα，GR，PR，AR和 - 最近 - PPARγ。前四个NR提到的是绝大多数与NR相关的同行研究的研究，而基本上是FDA批准的NR靶向疗法的一半。人类中有48个NR蛋白家族成员，其余的NR尚待学习。至少有两种方法可以找到NR类的药物机会。一种是找到与经过良好研究的NR和TF协同作用或调节的NR，这构成了转录调节剂之间转录串扰的发现和表征，这意味着对已知疾病靶标的相互作用者的“结合有罪”功能。另一种方法是识别由主要的细胞信号通路激活的NR，或信号转导级联的NRS“效应子”。为了丰富我们对可药物基因组的理解，我们建议在不同的信号事件下收集有关NR激活和NR串扰动力学的数据。我们的第一个主要目标（AIM 1）是向具有已知作用但没有完全表征机制的NR学习。我们将使用转录因子响应元件下拉（cattfre） - 我们最近开发的一种方法，用于将TF DNA结合活性直接分析与其同源DNA响应元件 - 以发现针对已知NRS更好的小分子调节剂的细胞响应的转录效应（NRS，其核心测量剂和其他相互作用的转录因子）。一种完整的方法是使用Cattfre技术查找由7个主要信号通路激活的NR，TFS和核心调节器（AIM 2）。这项研究将将信号事件与NR激活联系起来，并在理解信号通路的整体综合转录影响方面截断。 AIM 2数据将在肺癌病理学的背景下收集。 AIM 3开发信息解决方案可以解决从AIM 1和2获得的数据中的技术和生物医学分析问题。