DEVELOPMENT OF TRANSLATION STATE ARRAY ANALYSIS

平移状态数组分析的发展

• 批准号: 6287917
• 负责人: DAVID R MORRIS
• 金额: $ 67.45万
• 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/6287917
• 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：（申请人的描述） 随着全基因组序列的迅速扩大， 分析全基因组表达模式的潜力正在获得 现实 这一庞大的比较数据库的可用性将使 对癌症研究的重大影响。 显然，这些成功 方法的关键取决于能够定义之间的关系 表达模式和下游事件定义表型。在大多 部分，这些下游事件是由生物活性介导的， 蛋白质分子，这反过来又由蛋白质水平控制， 翻译后修饰 在这个应用中，我们提出了一个第二代方案的mRNA 表情分析为了发展这项技术，同时 产生生物学上重要的信息，我们选择作为我们的模型， 酵母菌基因表达的细胞周期调控 啤酒。这项提案的核心技术是我们所说的 翻译状态阵列分析（translation state array analysis，TSSA） 除此之外，TSSA还提供 单个mRNA分子的绝对水平， 这些mRNA参与蛋白质合成。TSAA的结果将 与蛋白质组学分析生成的数据集相关。组合这些 三个测量（总mRNA，翻译的mRNA和蛋白质水平）， 同样的生物系统将使我们能够对详细的陈述 特定基因的调控机制，并确定基因簇 都是通过相同的机制来调节的。这项研究将提供更多 精心打磨的高通量工具，可深入了解这两种机制 单个基因的调节以及蛋白质的水平和活性 最终决定了表型。