RNA Dynamics of Transient Macromolecular Complexes in Cancer Cells

描述（由申请人提供）：在本提案中，我们试图了解瞬态细胞复合物中的RNA动力学，并利用该信息识别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颗粒的蛋白质成分，但由于缺乏合适的技术，尚未对RNA群体动态进行定量分析。我们的实验室专注于rna结合蛋白（rbp）和microRNAs调控的mrna群体的多靶点。我们已经证明，序列特异性rbp协调调节功能相关的mrna组，这些rbp在小分子药物激活细胞时被重塑。例如，与几种rbp相关的mrna已被证明在用视黄酸或白血病来源的免疫T细胞用磷酸酯加丝裂原治疗胚胎癌细胞后协同改变。我们假设，这些瞬时RNP复合物中RNA群体的动态变化协调了mrna的功能相关亚群，这些亚群编码的蛋白质同步表达是致癌转化所必需的。在这里，我们将使用一种概率方法来量化动态RNA变化，以分析在恶性肿瘤进展过程中与rbp相关的mrna。我们将使用Robert Weinberg实验室开创性的方法，利用RAS、端粒酶和其他四种转化蛋白，量化与HuR（早期反应基因mrna）、TIAR（应激颗粒rna）和AGO2/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前状态的转变。HuR向细胞质的转移改变了mRNA的靶向性，并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，并确定它们如何随着转化表型的逐渐发展以及诱导氧化应激和缺氧而变化。rnp -免疫沉淀微阵列（RIP-Chip）程序将用于鉴定和量化与特定rbp相关的mrna；我们的合作者Thomas Tuschl最近在实验室开发了一种高特异性和高效率的紫外线交联方法，称为PAR-CLIP，将用于鉴定这些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结合位点全球整合。我们将构建这些事件的定量动态模型，并使用这些数据查询影响这些过程的药物基因组连接图，如我们最近发表的文章所示。这些化合物将用于进一步研究该系统中潜在的致癌生物学。每个进展阶段的显微镜可视化将用于确认RNA/RBP定位和药物治疗的表型效应。我们的长期计划是利用RNA靶向的定量概率方法，利用癌症动物模型来研究这些和其他参与转录后基因表达的瞬时大分子RNP复合物。

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.