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MicroRNA-Based Detection of Barriers to Melanoma Progression

MicroRNA-Based Detection of Barriers to Melanoma Progression
基于 MicroRNA 的黑色素瘤进展障碍检测
批准号:
8927090
负责人:
Robert Laird Judson-Torres
金额:
$39.04万
依托单位国家:
美国
项目类别:
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-12 至 2019-08-31

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项目成果

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中文摘要
翻译
描述(由申请人提供):复杂多细胞生物体(如哺乳动物)的一个特别方面是来自同一基因组的不同类型细胞的多样性。每种类型的细胞或细胞状态在形态、功能和生理学上都有很大的不同,必须在生物体的发育和日常生活中仔细调节。这包括严格控制细胞状态转换和稳定细胞状态以抵抗外部刺激和内部干扰,包括基因组突变。特定细胞状态的基础是表达基因的网络。虽然现在用一个实验来测量一个细胞群体中每个基因的表达已经很普遍了,但人们对一个大型网络中的单个基因如何共同发挥作用却知之甚少。这在复杂的人类疾病中尤其如此,例如癌症,其中来自单个肿瘤的细胞群可以包含许多不同的细胞状态。该项目的目的是识别和功能性剖析相互作用的基因网络,这些基因稳定黑色素细胞和早期黑色素瘤,防止其转变为更晚期的疾病状态。核心方法是基于microRNA的功能基因网络检测。microRNA(miRNAs)是从基因组转录的,但不编码蛋白质,而是调节mRNA网络的翻译和稳定性。最近的研究表明,miRNAs是识别调控细胞状态转换的基因网络的极好工具。这些基因的共调控网络富集了遗传相互作用,这可以使用最近适用于哺乳动物细胞的映射技术从它们以前在单细胞生物体中的广泛使用中鉴定。在本申请的第一个目的中,这些方法将用于研究不同的黑素瘤驱动突变(启动黑素瘤进展的基因组的那些变化)与针对进一步进展稳定的基因网络之间的关系,测试不同的启动事件导致肿瘤发生的不同多基因屏障的假设。已知促进黑色素瘤的miRNA将被引入一组具有不同驱动突变的小鼠黑色素细胞中。当诱导进展时,将通过实验确定miRNA靶向的基因网络,并研究它们的个体功能和遗传相互作用。在第二个目标中,将使用原代患者样本和重建的皮肤培养物来测试在表达和功能方面与人黑素瘤进展相关的miRNA靶向网络。在第三个目标中,将在单细胞水平上分析肿瘤细胞群体。将测定每个细胞进展或转变为治疗抗性细胞状态的能力,并测量与每个转变相关的miRNA谱。总的来说,这些方法将提高我们对共同预防黑色素瘤进展的基因网络的了解,增加我们对这种致命疾病进行有意义的个性化治疗的潜力。
英文摘要
DESCRIPTION (provided by applicant): An extraordinary aspect of complex multicellular organisms, such as mammals, is the diversity of different types of cells that arise from the same genome. Each type of cell, or cell state, differs drastically in morphology, function, and physiology and must be carefully regulated during both the development and the daily life of the organism. This includes both tight control of cell state transitions and the stabilization of cell states against external stimuli and internal disturbances, including genomic mutations. Underlying specific cell states are networks of expressed genes. Although it is now commonplace to measure the expression of every gene within a population of cells with a single experiment, little is known about how the individual genes within a large network function together. This is especially true in complex human diseases, such as cancer, where the population of cells from a single tumor can contain many different cell states. The objective of this project is to both identify and to functionally dissect networks of interacting genes that stabilize melanocytes and early- stage melanomas against transitions into more advanced disease states. The central approach is microRNA- based detection of functional gene networks. MicroRNAs (miRNAs) are transcribed from the genome but do not encode for proteins, rather regulating the translation and stability of mRNA networks. It has recently been shown that miRNAs serve as excellent tools for identifying networks of genes that regulate cell state transitions. These co-regulated networks of genes are enriched for genetic interactions, which can be identified using mapping techniques recently adapted for mammalian cells from their previous extensive use in single cell organisms. In the first aim of this application, these methods will be used to investigate the relationship between different melanoma driver mutations (those changes to the genome that initiate melanoma progression) and the networks of genes that stabilize against further progression, testing the hypothesis that different initiatig events result in distinct multigenic barriers to tumorigenesis. MiRNAs known to advance melanoma will be introduced into a panel of mouse melanocytes with different driver mutations. When progression is induced, the network of genes targeted by the miRNA will be experimentally determined and their individual function and genetic interactions investigated. In the second aim, the miRNA-targeted networks that correlate with human melanoma progression in both expression and function will be tested using primary patient samples and reconstituted skin culture. In the third aim, populations of tumor cells will be analyzed on the single cell leve. Each cell will be assayed for its ability to progress or to transition into a therapy-resistant cel state, and the miRNA profiles associated with each transition will be measured. Collectively, these approaches will improve our knowledge of the networks of genes that work together to prevent melanoma progression, increasing our potential to conduct meaningful personalized therapies for this deadly disease.
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