DEVELOPMENT OF TRANSLATION STATE ARRAY ANALYSIS

平移状态数组分析的发展

• 批准号: 6498013
• 负责人: DAVID R MORRIS
• 金额: $ 75.73万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-08 至 2004-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6498013
• 关键词: Saccharomyces cerevisiae; cell cycle; cell cycle proteins; fungal genetics; fungal proteins; gene induction /repression; genetic translation; mass spectrometry; messenger RNA; method development; microarray technology; microorganism culture; molecular biology information system; monoclonal antibody; nucleic acid probes; open reading frames; protein biosynthesis; synchronous cell division; temperature sensitive mutant

DESCRIPTION: (Applicant's Description) With the rapidly expanding availability of entire genome sequences, the potential for analyzing whole-genome expression patterns is attaining reality. The availability of this vast pool of comparative data will have a major impact on cancer research. Clearly, however, the success of these approaches depends critically on being able to define the relationship between expression patterns and downstream events that define phenotype. For the most part, these downstream events are mediated by the biological activities of protein molecules, which are in turn controlled by protein level and post-translational modification. In this application, we propose to develop a second generation scheme for mRNA expression analysis. In order to develop this technology and at the same time generate biologically important information, we have chosen as our model cell-cycle regulation of gene expression in the yeast Saccharomyces cerevisiae. The core technology in this proposal is what we have termed translation state array analysis (TSSA). TSSA provides, in addition to the absolute levels of individual mRNA molecules, information on the degree to which these mRNAs are engaged in protein synthesis. The results from TSAA will be correlated with datasets generated from proteomic analysis. Combining these three measurements (total mRNA, translated mRNA and protein level) from the same biological system will enable us to make statements about detailed mechanisms of regulation of specific genes and also identify clusters of genes that are regulated through the same mechanisms. This study will provide more finely honed high-throughput tools to provide insight into both mechanisms of regulation of individual genes and the levels and activities of the proteins that ultimately dictate phenotype.

描述：(申请人描述) 随着整个基因组序列可获得性的迅速扩大， 分析全基因组表达模式的潜力正在实现 现实。这一巨大的比较数据池的可用性将产生 对癌症研究产生重大影响。然而，显然，这些项目的成功 方法关键取决于是否能够定义 定义表型的表达模式和下游事件。最多的 部分，这些下游事件是由生物活性调节的 蛋白质分子，而蛋白质分子又受蛋白质水平和 翻译后修饰。 在这个应用中，我们建议开发第二代mRNA方案 表情分析。为了发展这项技术，同时 产生生物上重要的信息，我们选择作为我们的模型 酵母中基因表达的细胞周期调控 酿酒。这项提案中的核心技术就是我们所说的 转换状态数组分析(TSSA)。TSSA除了提供 单个mRNA分子的绝对水平，关于程度的信息 这些mRNAs参与蛋白质合成。TSAA的结果将 与蛋白质组分析产生的数据集相关联。将这些组合在一起 三种测量(总mRNA、翻译mRNA和蛋白质水平)来自 同样的生物系统将使我们能够详细地陈述 特定基因的调节机制以及识别基因簇 都是通过相同的机制进行监管的。这项研究将提供更多 经过精心打磨的高吞吐量工具，可深入了解 对单个基因和蛋白质水平和活性的调节 这最终决定了表型。