Gene Expression Levels Across Diverse Genomes

Stanford University, Stanford CA

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Abstract

[unreadable] DESCRIPTION (provided by applicant): Prokaryotic and eukaryotic whole genome sequence data is accumulating at an unprecedented pace. The next phase will be increasingly dominated by efforts to characterize, categorize, and analyze these data with the goal of understanding molecular sequence information and its significance in biological systems. Much current biological and medical research centers on DNA microarrays. The main focus of our research is to evaluate gene expression levels based on codon usage. Our sequence methods are complementary to the experimental procedures of 2D-gel electrophoresis in assessing gene expression levels. We have introduced a theoretical computational method for characterizing gene expression levels based on codon usage differences between gene classes. The method has been applied to a variety of genomes including fast-growing bacteria, the cyanobacterium of Synechocystis PCC6803, and the radiation resistant Deinococcus radiodurans (see Progress Report). We can predict highly expressed genes in each bacterial genome, which correlate very well with 2D-gel protein abundances. We propose to apply the methods to all complete genomes and illustrate here pilot studies for two groups of bacterial genomes: the first group consists of all available low G+C Gram-positive genomes including the pathogens Listeria monocytogenes, Staphylococcus aureus, Streptococcus pyogenes, and the nonpathogenic dairy fermentation bacterium Lactococcus lactis. The second group consists of all available high G+C a-proteobacteria. The latter genomes are important for understanding nitrogen fixation. A second aspect of our research will be to investigate the status of genes in several metabolic pathways and of several protein families among archaeal and bacterial species contrasting presence, absence, and expression levels of genes. A third major objective of our research will be to extend our codon usage methods for predicting gene expression levels to eukaryotic genomes, including yeast, D. melanogaster, C. elegans, and human. [unreadable] [unreadable]