RNA Dynamics of Transient Macromolecular Complexes in Cancer Cells

描述(申请人提供)：在这项提案中，我们试图了解瞬时细胞复合体中的RNA动力学，并利用这些信息来识别可能调节恶性转化途径的候选药物。虽然在哺乳动物细胞中已经研究了RNP颗粒的蛋白质成分，但由于缺乏合适的技术，还没有对定量RNA种群动力学进行分析。我们的实验室专注于RNA结合蛋白(RBPs)和microRNAs调控的mRNAs群体的多靶点研究。我们已经证明了序列特异性RBPs协调调节一组功能相关的mRNAs，并且这些RNPs在小分子药物激活细胞时被重塑。例如，与几种限制性商业惯例相关的mRNAs已被证明在用维甲酸处理胚胎癌细胞或用佛波酯加有丝分裂原处理白血病来源的免疫T细胞后发生协调变化。我们假设，这些瞬时RNP复合体中RNA群体的动态变化协调了编码蛋白质的mRNAs的功能相关子集，这些蛋白质的同步表达是致癌转化所必需的。在这里，我们将使用一种量化动态RNA变化的概率方法来分析与RBPs恶变过程相关的mRNAs。我们将使用Robert Weinberg实验室首创的方法来研究原代上皮细胞从癌前状态到癌症状态的转变，方法包括RAS、端粒酶和其他四种转化蛋白，以量化与HUR(早期反应基因mRNAs)、TIAR(应激颗粒RNAs)和AGO2/RISC(处理“P”身体RNAs)相关的RNA的动态变化。HUR向细胞质的转移改变了其信使核糖核酸的靶向，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体中的mRNAs和microRNAs的水平，并确定它们如何随着转化表型的逐渐发展以及在诱导氧化应激和缺氧后的变化。RNP免疫沉淀微阵列(RIP-Chip)程序将被用于识别和定量与特定限制性商业惯例相关的mRNAs；我们的合作者Thomas Tuschl的实验室最近开发的一种称为PAR-CLIP的高特异性和高效的紫外光交联法将被用于识别这些限制性商业惯例和microRNAs的精确结合位点。MicroRNA与mRNAs结合位点的动态变化将与RBP结合位点在全球范围内整合。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影响这些过程的药物-基因组连接图，正如我们最近的出版物所展示的那样。这些化合物将被用来进一步研究这个系统中致癌的潜在生物学。每个进展阶段的显微可视化将用于确认RNA/RBP的定位和药物治疗的表型效应。我们的长期计划是使用这种RNA靶向的定量概率方法来研究这些和其他参与转录后基因表达的瞬时大分子RNP复合体，使用癌症动物模型。

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.