Genes optimized by evolution for accurate and fast translation encode in Archaea and Bacteria a broad and characteristic spectrum of protein functions

Abstract

Background: In many microbial genomes, a strong preference for a small number of codons can be observed in genes whose products are needed by the cell in large quantities. This codon usage bias (CUB) improves translational accuracy and speed and is one of several factors optimizing cell growth. Whereas CUB and the overrepresentation of individual proteins have been studied in detail, it is still unclear which high-level metabolic categories are subject to translational optimization in different habitats.

Results: In a systematic study of 388 microbial species, we have identified for each genome a specific subset of genes characterized by a marked CUB, which we named the effectome. As expected, gene products related to protein synthesis are abundant in both archaeal and bacterial effectomes. In addition, enzymes contributing to energy production and gene products involved in protein folding and stabilization are overrepresented. The comparison of genomes from eleven habitats shows that the environment has only a minor effect on the composition of the effectomes. As a paradigmatic example, we detailed the effectome content of 37 bacterial genomes that are most likely exposed to strongest selective pressure towards translational optimization. These effectomes accommodate a broad range of protein functions like enzymes related to glycolysis/gluconeogenesis and the TCA cycle, ATP synthases, aminoacyl-tRNA synthetases, chaperones, proteases that degrade misfolded proteins, protectants against oxidative damage, as well as cold shock and outer membrane proteins.

Conclusions: We made clear that effectomes consist of specific subsets of the proteome being involved in several cellular functions. As expected, some functions are related to cell growth and affect speed and quality of protein synthesis. Additionally, the effectomes contain enzymes of central metabolic pathways and cellular functions sustaining microbial life under stress situations. These findings indicate that cell growth is an important but not the only factor modulating translational accuracy and speed by means of CUB.

Figure 1

A plot of $\overline{GC{B}_{Eff}}$-scores versus the number of tRNA genes and the minimum generation time for microbial species. Panel A: For 388 microbial species constituting the set MG_CUB the number of tRNA genes and the $\overline{GC{B}_{Eff}}$-score was determined as described and plotted. A Spearman rank correlation confirms the statistically significant correlation of these two values (r_s = 0.71, p < 0.001). Panel B: The minimum generation time for 113 species of MG_CUB (as listed in [13]) was plotted versus the $\overline{GC{B}_{Eff}}$-score. A Spearman rank correlation confirms the statistically significant correlation of these two values (r_s = -0.75, p < 0.001).

Figure 2

Abundance of metabolic functions in archaeal and bacterial effectomes. For all FunCat categories of level 1, the Abund_Eff-values were deduced from the datasets MG_CUB(Archaea) and MG_CUB(Bacteria). Positive scores indicate categories overrepresented in the effectomes. Underrepresented categories have negative values. The categories are numbered according to the FunCat scheme: "metabolism" (1), "energy" (2), "cell cycle and DNA processing" (10), "transcription" (11), "protein synthesis" (12), "protein fate (folding, modification, destination) " (14), "regulation of metabolism and protein function" (18), "cellular transport, transport facilitation and transport routes" (20), "cellular communication/signal transduction mechanism" (30), "cell rescue, defense and virulence" (32), "interaction with the environment" (34), "transposable elements, viral and plasmid proteins" (38), "cell fate" (40), "development (systemic) " (41), "biogenesis of cellular components" (42), "subcellular location" (70). Abund_Eff-values were plotted if the number #_All(Cat) was at least 100 (compare Table 1).

Figure 3

Habitat specific abundance of metabolic functions in bacterial effectomes. For FunCat categories of level 2, the Abund_Eff-values were deduced for specific bacterial subsets. Scores larger than zero indicate categories overrepresented in the effectomes. Underrepresented categories have negative values. The categories are numbered according to the FunCat scheme: "amino acid metabolism" (1.01), "secondary metabolism" (1.20), "extracellular metabolism" (1.25), "glycolysis and gluconeogenesis" (2.01), "glyoxylate cycle" (2.04), "pentose-phosphate pathway" (2.07), "pyruvate dehydrogenase complex" (2.08), "anaplerotic reactions" (2.09), "tricarboxylic-acid pathway (citrate cycle, Krebs cycle, TCA cycle)" (2.10), "electron transport and membrane-associated energy conservation" (2.11), "metabolism of energy reserves (e.g. glycogen, trehalose)" (2.19), "oxidation of fatty acids" (2.25), "photosynthesis" (2.30), "energy conversion and regeneration" (2.45), "DNA processing " (10.01), "RNA synthesis" (11.02), "RNA processing" (11.04), "RNA modification" (11.06), "ribosome biogenesis" (12.01), "translation" (12.04), "translational control" (12.07), "aminoacyl-tRNA synthetases" (12.10), "protein folding and stabilization" (14.01), "protein targeting, sorting and translocation" (14.04), "protein modification" (14.07), "protein/peptide degradation" (14.13), "transmembrane signal transduction" (30.05), "stress response" (32.01), "disease, virulence and defense" (32.05), "cellular sensing and response to external stimulus" (34.11), "prokaryotic cell envelope structures" (42.34), "fungal/microorganismic cell type differentiation" (43.01), "cell wall" (70.01). Abund_Eff -values were plotted if the number #All(Cat) was at least 100. Abbreviations of subsets: AB all, TH thermophilic, MS mesophilic, PS psychrophilic, AER aerobic, ANE anaerobic, AQU aquatic, TER terrestrial, MHAL moderately halophilic bacteria, and HITR the subset of Bacteria possessing an extreme number of tRNA genes represented by MG_CUB(Bacteria_HITR).