1887

Abstract

The are a large family of Proteobacteria that include many well-known prokaryotic genera, such as , and . The main ideas of synonymous codon usage (CU) evolution and translational selection have been deeply influenced by studies with these bacterial groups. In this work we report the analysis of the CU pattern of completely sequenced bacterial genomes that belong to the . The effect of selection in translation acting at the levels of speed and accuracy, and phylogenetic trends within this group are described. Preferred (optimal) codons were identified. The evolutionary dynamics of these codons were studied and following a Bayesian approach these preferences were traced back to the common ancestor of the family. We found that there is some level of variation in selection among the analysed micro-organisms that is probably associated with lineage-specific trends. The codon bias was largely conserved across the evolutionary time of the family in highly expressed genes and protein conserved regions, suggesting a major role of negative selection. In this sense, the results support the idea that the extant CU bias is finely tuned over the ancestral well-conserved pool of tRNAs.

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2013-03-01
2022-01-22
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References

  1. Akashi H. ( 1994). Synonymous codon usage in Drosophila melanogaster: natural selection and translational accuracy. Genetics 136:927–935[PubMed]
    [Google Scholar]
  2. Botzman M., Margalit H. ( 2011). Variation in global codon usage bias among prokaryotic organisms is associated with their lifestyles. Genome Biol 12:R109 [View Article][PubMed]
    [Google Scholar]
  3. Charif D., Thioulouse J., Lobry J. R., Perrière G. ( 2005). Online synonymous codon usage analyses with the ade4 and seqinR packages. Bioinformatics 21:545–547 [View Article][PubMed]
    [Google Scholar]
  4. Chen S. L., Lee W., Hottes A. K., Shapiro L., McAdams H. H. ( 2004). Codon usage between genomes is constrained by genome-wide mutational processes. Proc Natl Acad Sci U S A 101:3480–3485 [View Article][PubMed]
    [Google Scholar]
  5. Drummond D. A., Wilke C. O. ( 2008). Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134:341–352 [View Article][PubMed]
    [Google Scholar]
  6. Edgar R. C. ( 2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797 [View Article][PubMed]
    [Google Scholar]
  7. Eyre-Walker A. ( 1996). Synonymous codon bias is related to gene length in Escherichia coli: selection for translational accuracy?. Mol Biol Evol 13:864–872 [View Article][PubMed]
    [Google Scholar]
  8. Grantham R., Gautier C., Gouy M., Mercier R., Pavé A. ( 1980). Codon catalog usage and the genome hypothesis. Nucleic Acids Res 8:r49–r62 [View Article][PubMed]
    [Google Scholar]
  9. Guindon S., Gascuel O. ( 2003). A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52:696–704 [View Article][PubMed]
    [Google Scholar]
  10. Herbeck J. T., Wall D. P., Wernegreen J. J. ( 2003). Gene expression level influences amino acid usage, but not codon usage, in the tsetse fly endosymbiont Wigglesworthia . Microbiology 149:2585–2596 [View Article][PubMed]
    [Google Scholar]
  11. Hershberg R., Petrov D. A. ( 2008). Selection on codon bias. Annu Rev Genet 42:287–299 [View Article][PubMed]
    [Google Scholar]
  12. Higgs P. G., Ran W. ( 2008). Coevolution of codon usage and tRNA genes leads to alternative stable states of biased codon usage. Mol Biol Evol 25:2279–2291 [View Article][PubMed]
    [Google Scholar]
  13. Ikemura T. ( 1985). Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol 2:13–34[PubMed]
    [Google Scholar]
  14. Iriarte A., Baraibar J. D., Romero H., Musto H. ( 2011). Selected codon usage bias in members of the class Mollicutes. Gene 473:110–118 [View Article][PubMed]
    [Google Scholar]
  15. Ishihama Y., Schmidt T., Rappsilber J., Mann M., Hartl F. U., Kerner M. J., Frishman D. ( 2008). Protein abundance profiling of the Escherichia coli cytosol. BMC Genomics 9:102 [View Article][PubMed]
    [Google Scholar]
  16. Kahali B., Basak S., Ghosh T. C. ( 2008). Delving deeper into the unexpected correlation between gene expressivity and codon usage bias of Escherichia coli genome. J Biomol Struct Dyn 25:655–661 [View Article][PubMed]
    [Google Scholar]
  17. Lowe T. M., Eddy S. R. ( 1997). tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 25:955–964[PubMed] [CrossRef]
    [Google Scholar]
  18. McGaughran A., Holland B. R. ( 2010). Testing the effect of metabolic rate on DNA variability at the intra-specific level. PLoS ONE 5:e9686 [View Article][PubMed]
    [Google Scholar]
  19. Naum M., Brown E. W., Mason-Gamer R. J. ( 2008). Is 16S rDNA a reliable phylogenetic marker to characterize relationships below the family level in the Enterobacteriaceae?. J Mol Evol 66:630–642 [View Article][PubMed]
    [Google Scholar]
  20. Novembre J. A. ( 2002). Accounting for background nucleotide composition when measuring codon usage bias. Mol Biol Evol 19:1390–1394 [View Article][PubMed]
    [Google Scholar]
  21. Pagel M., Meade A. ( 2006). Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo. Am Nat 167:808–825 [View Article][PubMed]
    [Google Scholar]
  22. Pagel M., Meade A., Barker D. ( 2004). Bayesian estimation of ancestral character states on phylogenies. Syst Biol 53:673–684 [View Article][PubMed]
    [Google Scholar]
  23. Paradis E., Claude J., Strimmer K. ( 2004). APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 20:289–290 [View Article][PubMed]
    [Google Scholar]
  24. Paradis S., Boissinot M., Paquette N., Bélanger S. D., Martel E. A., Boudreau D. K., Picard F. J., Ouellette M., Roy P. H., Bergeron M. G. ( 2005). Phylogeny of the Enterobacteriaceae based on genes encoding elongation factor Tu and F-ATPase β-subunit. Int J Syst Evol Microbiol 55:2013–2025 [View Article][PubMed]
    [Google Scholar]
  25. Pham H. N., Ohkusu K., Mishima N., Noda M., Monir Shah M., Sun X., Hayashi M., Ezaki T. ( 2007). Phylogeny and species identification of the family Enterobacteriaceae based on dnaJ sequences. Diagn Microbiol Infect Dis 58:153–161 [View Article][PubMed]
    [Google Scholar]
  26. Rambaut A., Drummond A. J. ( 2007). http://beast.bio.ed.ac.uk/Tracer
  27. Retchless A. C., Lawrence J. G. ( 2011). Quantification of codon selection for comparative bacterial genomics. BMC Genomics 12:374 [View Article][PubMed]
    [Google Scholar]
  28. Rice P., Longden I., Bleasby A. ( 2000). EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 16:276–277 [View Article][PubMed]
    [Google Scholar]
  29. Rispe C., Delmotte F., van Ham R. C., Moya A. ( 2004). Mutational and selective pressures on codon and amino acid usage in Buchnera, endosymbiotic bacteria of aphids. Genome Res 14:44–53 [View Article][PubMed]
    [Google Scholar]
  30. Sharp P. M., Li W. H. ( 1986). Codon usage in regulatory genes in Escherichia coli does not reflect selection for ‘rare’ codons. Nucleic Acids Res 14:7737–7749 [View Article][PubMed]
    [Google Scholar]
  31. Sharp P. M., Li W. H. ( 1987a). The codon adaptation index – a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15:1281–1295 [View Article][PubMed]
    [Google Scholar]
  32. Sharp P. M., Li W. H. ( 1987b). The rate of synonymous substitution in enterobacterial genes is inversely related to codon usage bias. Mol Biol Evol 4:222–230[PubMed]
    [Google Scholar]
  33. Sharp P. M., Bailes E., Grocock R. J., Peden J. F., Sockett R. E. ( 2005). Variation in the strength of selected codon usage bias among bacteria. Nucleic Acids Res 33:1141–1153 [View Article][PubMed]
    [Google Scholar]
  34. Sharp P. M., Emery L. R., Zeng K. ( 2010). Forces that influence the evolution of codon bias. Philos Trans R Soc Lond B Biol Sci 365:1203–1212 [View Article][PubMed]
    [Google Scholar]
  35. Singer G. A., Hickey D. A. ( 2000). Nucleotide bias causes a genomewide bias in the amino acid composition of proteins. Mol Biol Evol 17:1581–1588 [View Article][PubMed]
    [Google Scholar]
  36. Stoletzki N., Eyre-Walker A. ( 2007). Synonymous codon usage in Escherichia coli: selection for translational accuracy. Mol Biol Evol 24:374–381 [View Article][PubMed]
    [Google Scholar]
  37. Sukumaran J., Holder M. T. ( 2010). DendroPy: a Python library for phylogenetic computing. Bioinformatics 26:1569–1571 [View Article][PubMed]
    [Google Scholar]
  38. Supek F., Vlahovicek K. ( 2004). INCA: synonymous codon usage analysis and clustering by means of self-organizing map. Bioinformatics 20:2329–2330 [View Article][PubMed]
    [Google Scholar]
  39. Supek F., Vlahovicek K. ( 2005). Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity. BMC Bioinformatics 6:182 [View Article][PubMed]
    [Google Scholar]
  40. Supek F., Skunca N., Repar J., Vlahovicek K., Smuc T. ( 2010). Translational selection is ubiquitous in prokaryotes. PLoS Genet 6:e1001004 [View Article][PubMed]
    [Google Scholar]
  41. Suzuki H., Brown C. J., Forney L. J., Top E. M. ( 2008). Comparison of correspondence analysis methods for synonymous codon usage in bacteria. DNA Res 15:357–365 [View Article][PubMed]
    [Google Scholar]
  42. Talavera G., Castresana J. ( 2007). Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol 56:564–577 [View Article][PubMed]
    [Google Scholar]
  43. Thioulouse J., Chessel D., Dole’dec S., Olivier J.-M. ( 1997). ADE-4: a multivariate analysis and graphical display software. Stat Comput 7:75–83 [View Article]
    [Google Scholar]
  44. Toth I. K., Pritchard L., Birch P. R. ( 2006). Comparative genomics reveals what makes an enterobacterial plant pathogen. Annu Rev Phytopathol 44:305–336 [View Article][PubMed]
    [Google Scholar]
  45. Vieira-Silva S., Rocha E. P. ( 2008). An assessment of the impacts of molecular oxygen on the evolution of proteomes. Mol Biol Evol 25:1931–1942 [View Article][PubMed]
    [Google Scholar]
  46. Wang B., Shao Z. Q., Xu Y., Liu J., Liu Y., Hang Y. Y., Chen J. Q. ( 2011). Optimal codon identities in bacteria: implications from the conflicting results of two different methods. PLoS ONE 6:e22714 [View Article][PubMed]
    [Google Scholar]
  47. Wernegreen J. J., Funk D. J. ( 2004). Mutation exposed: a neutral explanation for extreme base composition of an endosymbiont genome. J Mol Evol 59:849–858 [View Article][PubMed]
    [Google Scholar]
  48. Wertz J. E., Goldstone C., Gordon D. M., Riley M. A. ( 2003). A molecular phylogeny of enteric bacteria and implications for a bacterial species concept. J Evol Biol 16:1236–1248 [View Article][PubMed]
    [Google Scholar]
  49. Withers M., Wernisch L., dos Reis M. ( 2006). Archaeology and evolution of transfer RNA genes in the Escherichia coli genome. RNA 12:933–942 [View Article][PubMed]
    [Google Scholar]
  50. Yang Z. ( 2007). PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 24:1586–1591 [View Article][PubMed]
    [Google Scholar]
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