Abstract
Metabolic efficiency, as a selective force shaping proteomes, has been shown to exist in Escherichia coli and Bacillus subtilis and in a small number of organisms with photoautotrophic and thermophilic lifestyles. Earlier attempts at larger-scale analyses have utilized proxies (such as molecular weight) for biosynthetic cost, and did not consider lifestyle or auxotrophy. This study extends the analysis to all currently sequenced microbial organisms that are amenable to these analyses while utilizing lifestyle specific amino acid biosynthesis pathways (where possible) to determine protein production costs and compensating for auxotrophy. The tendency for highly expressed proteins (with adherence to codon usage bias as a proxy for expressivity) to utilize less biosynthetically expensive amino acids is taken as evidence of cost selection. A comprehensive analysis of sequenced genomes to identify those that exhibit strong translational efficiency bias (389 out of 1,700 sequenced organisms) is also presented.
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References
Akashi H, Gojobori T (2002) Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc Natl Acad Sci USA 19:3695–3700
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410
Alves R, Savageau MA (2005) Evidence of selection for low cognate amino acid bias in amino acid biosynthetic enzymes. Mol Microbiol 56:1017–1034
Barton MD, Delneri D, Oliver SG, Rattray M, Bergman CM (2010) Evolutionary systems biology of amino acid biosynthetic cost in yeast. PLoS ONE 5:e11935
Bragg JG, Wagner A (2009) Protein material costs: single atoms can make an evolutionary difference. Trends Genet 25:5–8
Carbone A, Zinovyev A, Kepes F (2003) Codon adaptation index as a measure of dominating codon bias. Bioinformatics 19:2005–2015
Carbone A, Kepes F, Zinovyev A (2005) Codon bias signatures, organization of microorganisms in codon space, and lifestyle. Mol Biol Evol 22:547–561
Chanda I, Pan A, Dutta C (2005) Proteome composition in Plasmodium falciparum: higher usage of GC-rich nonsynonymous codons in highly expressed genes. J Mol Evol 61:513–523
Craig CL, Weber RS (1998) Selection costs of amino acid substitutions in ColE1 and ColIa gene clusters harbored by Escherichia coli. Int J Biol Macromol 24:109–118
Das S, Ghosh S, Pan A, Dutta C (2005) Compositional variation in bacterial genes and proteins with potential expression level. FEBS Lett 579:5205–5210
dos Reis M, Wernisch L, Savva R (2003) Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome. Nucleic Acids Res 31:6976–6985
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
Felsenstein J (1989) PHYLIP—phylogeny inference package (Version 3.2). Cladistics 5:164–166
Felsenstein J (1993) PHYLIP (Phylogeny Inference Package) Distributed by the author:Seattle
Garat B, Musto H (2000) Trends of amino acid usage in the proteins from the unicellular parasite Giardia lamblia. Biochem Biophys Res Commun 279:996–1000
Garcia-Vallvé S, Guzman E, Montero MA, Romeu A (2003) HGT-DB: a database of putative horizontally transferred genes in prokaryotic complete genomes. Nucleic Acids Res 31:187–189
Heizer EM Jr, Raiford DW III, Raymer ML, Doom TE, Miller RV, Krane DE (2006) Amino acid cost and codon-usage biases in 6 prokaryotic genomes: a whole-genome analysis. Mol Biol Evol 23:1670–1680
Heizer EM Jr, Raymer ML, Krane DE (2011) Amino acid biosynthetic cost and protein conservation. J Mol Evol 72:466–473
Hershberg R, Petrov DA (2009) General rules for optimal codon choice. PLoS Genet 5:e1000556
Ikemura T (1981a) Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes. J Mol Biol 146:1–21
Ikemura T (1981b) Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system. J Mol Biol 151:389–409
Jansen R, Gerstein M (2000) Analysis of the yeast transcriptome with structural and functional categories: characterizing highly expressed proteins. Nucleic Acids Res 28:1481–1488
Kahali B, Basak S, Ghosh TC (2007) Reinvestigating the codon and amino acid usage of S. cerevisiae genome: a new insight from protein secondary structure analysis. Biochem Biophys Res Commun 354:693–699
Lawrence JG, Hendrickson H (2005) Genome evolution in bacteria: order beneath chaos. Curr Opin Microbiol 8:572–578
Lobry JR, Gautier C (1994) Hydrophobicity, expressivity and aromaticity are the major trends of no-acid usage in 999 Escherichia coli chromosome-encoded genes. Nucleic Acids Res 22:3174–3180
NCBI (2011) National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov/
Nei M (1975) Molecular population genetics and evolution. Front Biol 40:I-288
Palacios C, Wernegreen JJ (2002) A strong effect of AT mutational bias on amino acid usage in Buchnera is mitigated at high-expression genes. Mol Biol Evol 19:1575–1584
Peixoto L, Fernandez V, Musto H (2004) The effect of expression levels on codon usage in Plasmodium falciparum. Parasitology 128:245–251
Raiford DW, Heizer EM Jr, Miller RV, Akashi H, Raymer ML, Krane DE (2008) Do amino acid biosynthetic costs constrain protein evolution in Saccharomyces cerevisiae? J Mol Evol 67(6):621–630
Raiford DW, Krane DE, Doom TE, Raymer ML (2010) Automated isolation of translational efficiency bias that resists the confounding effect of GC(AT)-content. IEEE/ACM Trans Comput Biol Bioinform 7:238–250
Raiford DW, Krane DE, Doom TE, Raymer ML (2011) A genetic optimization approach for isolating translational efficiency bias. IEEE/ACM Trans Comput Biol Bioinform 8:342–352
Schaber J, Rispe C, Wernegreen J, Buness A, Delmotte F, Silva FJ, Moya A (2005) Gene expression levels influence amino acid usage and evolutionary rates in endosymbiotic bacteria. Gene 352:109–117
Seligmann H (2003) Cost-minimization of amino acid usage. J Mol Evol 56:151–161
Sharp PM, Li WH (1987) The codon adaptation index—a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15:1281–1295
Smith DR, Chapman MR (2010) Economical evolution: microbes reduce the synthetic cost of extracellular proteins. mBio 1(3): e00131
Spearman C (1904) General intelligence objectively determined and measured. Am J Psychol 15:201–293
Supek F, Vlahovicek K (2005) Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity. BMC Bioinform 6:182
Swire J (2007) Selection on synthesis cost affects interprotein amino acid usage in all three domains of life. J Mol Evol 64:558–571
Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680
Urrutia AO, Hurst LD (2003) The signature of selection mediated by expression on human genes. Genome Res 13:2260–2264
Wagner A (2005) Energy constraints on the evolution of gene expression. Mol Biol Evol 22:1365–1374
Zavala A, Naya H, Romero H, Musto H (2002) Trends in codon and amino acid usage in Thermotoga maritima. J Mol Evol 54:563–568
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Raiford, D.W., Heizer, E.M., Miller, R.V. et al. Metabolic and Translational Efficiency in Microbial Organisms. J Mol Evol 74, 206–216 (2012). https://doi.org/10.1007/s00239-012-9500-9
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DOI: https://doi.org/10.1007/s00239-012-9500-9