) 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.
