Post-transcriptional Expression Regulation in the Yeast Saccharomyces cerevisiae on a Genomic Scale

• Published in: Molecular & cellular proteomics Vol. 3; no. 11; pp. 1083 - 1092
• Main Authors: Beyer, Andreas, Hollunder, Jens, Nasheuer, Heinz-Peter, Wilhelm, Thomas
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2004; American Society for Biochemistry and Molecular Biology
• Subjects: Cell Compartmentation - genetics; Cell Compartmentation - physiology; Computational Biology; Gene Expression Regulation, Fungal - genetics; Gene Expression Regulation, Fungal - physiology; Genome, Fungal; Protein Biosynthesis - genetics; Protein Biosynthesis - physiology; Protein Denaturation - genetics; Protein Denaturation - physiology; Proteome; RNA Processing, Post-Transcriptional; RNA, Messenger - genetics; RNA, Messenger - metabolism; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism
• ISSN: 1535-9476; 1535-9484; 1535-9484
• DOI: 10.1074/mcp.M400099-MCP200

Based on large-scale data for the yeast Saccharomyces cerevisiae (protein and mRNA abundance, translational status, transcript length), we investigate the relation of transcription, translation, and protein turnover on a genome-wide scale. We elucidate variations between different spatial cell compartments and functional modules by comparing protein-to-mRNA ratios, translational activity, and a novel descriptor for protein-specific degradation (protein half-life descriptor). This analysis helps to understand the cell’s strategy to use transcriptional and post-transcriptional regulation mechanisms for managing protein levels. For instance, it is possible to identify modules that are subject to suppressed translation under normal conditions (“translation on demand”). In order to reduce inconsistencies between the datasets, we compiled a new reference mRNA abundance dataset and we present a novel approach to correct large microarray signals for a saturation bias. Accounting for ribosome density based on transcript length rather than ORF length improves the correlation of observed protein levels to translational activity. We discuss potential causes for the deviations of these correlations. Finally, we introduce a quantitative descriptor for protein degradation (protein half-life descriptor) and compare it to measured half-lives. The study demonstrates significant post-transcriptional control of protein levels for a number of different compartments and functional modules, which is missed when exclusively focusing on transcript levels.