RNA Dynamics of Transient Macromolecular Complexes in Cancer Cells

描述（由申请人提供）：在本提案中，我们试图了解瞬时细胞复合物中的RNA动力学，并使用该信息来鉴定可能调节恶性肿瘤途径的候选药物。虽然已经在哺乳动物细胞中研究了RNP颗粒的蛋白质组分，但由于缺乏合适的技术，尚未分析定量RNA群体动力学。我们的实验室专注于由RNA结合蛋白（RBP）和microRNA调控的mRNA群体的多靶向。我们已经证明，序列特异性RBP协调调节功能相关的mRNA组，并且这些RNP在用小分子药物激活细胞期间被重塑。例如，与几种RBP相关的mRNA已显示在用视黄酸处理胚胎癌细胞或用佛波醇酯加有丝分裂原处理白血病衍生的免疫T细胞后协调变化。我们假设，这些瞬时RNP复合物内的RNA群体的动态变化协调功能相关的mRNA亚群，这些亚群编码的蛋白质的同步表达是致癌转化所需的。在这里，我们将使用一种概率的方法，量化动态RNA的变化，分析与恶性进展过程中的RBP相关的mRNA。我们将使用Robert温伯格实验室开创的方法，用RAS、端粒酶和其他四种转化蛋白定量与HuR（早期反应基因mRNA）、TIAR（应激颗粒RNA）和AGO 2/RISC（加工“P”体RNA）相关的RNA的动态变化，研究原代上皮细胞从癌前状态到癌性状态的转变。HuR转移到细胞质改变了其mRNA靶向，并已被认为是遗传性乳腺癌的预后因素。我们将量化这些复合物中mRNA和microRNA的水平，并确定它们如何响应转化表型的逐步发展以及诱导氧化应激和缺氧后的变化。RNP-免疫沉淀微阵列（RIP-Chip）程序将用于识别和定量与特定RBP相关的mRNA;我们的合作者托马斯图舍尔（Tuschl）实验室最近开发的具有高特异性和高效率的紫外光交联程序PAR-CLIP将用于识别这些RBP和microRNA的精确结合位点。microRNA与mRNA结合位点的动态变化将与RBP结合位点整体整合。我们将构建这些事件的定量动态模型，并使用这些数据来查询药物基因组连接图，以寻找影响这些过程的药物，如我们最近的出版物所示。这些化合物将用于进一步研究该系统中致癌的潜在生物学。将使用每个进展阶段的显微镜可视化来确认药物治疗的RNA/RBP定位和表型效应。这是我们的长期计划，使用这种定量的概率方法的RNA靶向研究这些和其他瞬时大分子RNP复合物参与转录后基因表达的癌症动物模型。

DESCRIPTION (provided by applicant): In this proposal, we seek to understand RNA dynamics in transient cellular complexes and to use that information to identify candidate drugs that may modulate the pathways to malignancy. While protein components of RNP granules have been studied in mammalian cells, quantitative RNA population dynamics have not been analyzed for lack of suitable technologies. Our laboratory focuses on multi-targeting of populations of mRNAs regulated by RNA-binding proteins (RBPs) and microRNAs. We have demonstrated that sequence specific RBPs coordinately regulate groups of functionally related mRNAs and these RNPs are remodeled during activation of cells with small molecule drugs. For example, the mRNAs associated with several RBPs have been shown to change coordinately following treatment of embryonic carcinoma cells with retinoic acid or leukemia-derived immune T cells with phorbol esters plus mitogens. We hypothesize that dynamic changes in RNA populations within these transient RNP complexes coordinate functionally-related subsets of mRNAs that encode proteins whose synchronized expression is required for oncogenic transformation. Here, we will use a probabilistic approach that quantifies dynamic RNA changes en masse to analyze mRNAs associated with RBPs during progression to malignancy. We will examine the transition from a precancerous state to a cancerous state in primary epithelial cells using methods pioneered in the laboratory of Robert Weinberg with RAS, telomerase and four other transforming proteins quantify dynamic changes in RNAs associated with HuR (early response gene mRNAs), TIAR (stress granule RNAs) and AGO2/RISC (processing "P" body RNAs). HuR shift to the cytoplasm alters its mRNA targeting and has been claimed as a prognostic factor in hereditary breast cancer. We will quantify the levels of mRNAs and microRNAs in these complexes and determine how they change in response to progressive development of a transformed phenotype, as well as after inducing oxidative stress and hypoxia. The RNP-Immunoprecipitation microarray (RIP-Chip) procedure will be used to identify and quantify mRNAs associated with specific RBPs; an ultraviolet light crosslinking procedure with high specificity and efficiency termed PAR-CLIP recently developed in the laboratory of our collaborator, Thomas Tuschl, will be used to identify the precise binding sites of these RBPs and microRNAs. Dynamic changes in sites of microRNA binding to mRNAs will be globally integrated with RBP binding sites. We will construct a quantitative dynamic model of these events and use these data to query the drug-genome Connectivity Map for drugs that affect these processes as demonstrated in our recent publications. These compounds will be used to further investigate the underlying biology of carcinogenesis in this system. Microscopic visualization at each stage of progression will be used to confirm the RNA/RBP localization and phenotypic effects of drug treatments. It is our long-term plan to use this quantitative probabilistic approach of RNA targeting to investigate these and other transient macromolecular RNP complexes involved in posttranscriptional gene expression using animal models of cancer.