Abstract

Our approach in predicting gene expression levels relates to codon usage differences among gene classes. In prokaryotic genomes, genes that deviate strongly in codon usage from the average gene but are sufficiently similar in codon usage to ribosomal protein genes, to translation and transcription processing factors, and to chaperone-degradation proteins are predicted highly expressed (PHX). By these criteria, PHX genes in most prokaryotic genomes include those encoding ribosomal proteins, translation and transcription processing factors, and chaperone proteins and genes of principal energy metabolism. In particular, for the fast-growing species Escherichia coli, Vibrio cholerae, Bacillus subtilis, and Haemophilus influenzae, major glycolysis and tricarboxylic acid cycle genes are PHX. In Synechocystis, prime genes of photosynthesis are PHX, and in methanogens, PHX genes include those essential for methanogenesis. Overall, the three protein families-ribosomal proteins, protein synthesis factors, and chaperone complexes-are needed at many stages of the life cycle, and apparently bacteria have evolved codon usage to maintain appropriate growth, stability, and plasticity. New interpretations of the capacity of Deinococcus radiodurans for resistance to high doses of ionizing radiation is based on an excess of PHX chaperone-degradation genes and detoxification genes. Expression levels of selected classes of genes, including those for flagella, electron transport, detoxification, histidine kinases, and others, are analyzed. Flagellar PHX genes are conspicuous among spirochete genomes. PHX genes are positively correlated with strong Shine-Dalgarno signal sequences. Specific regulatory proteins, e.g., two-component sensor proteins, are rarely PHX. Genes involved in pathways for the synthesis of vitamins record low predicted expression levels. Several distinctive PHX genes of the available complete prokaryotic genomes are highlighted. Relationships of PHX genes with stoichiometry, multifunctionality, and operon structures are discussed. Our methodology may be used complementary to experimental expression analysis.