Codon Bias as a Means to Fine-Tune Gene Expression
2015; Elsevier BV; Volume: 59; Issue: 2 Linguagem: Inglês
10.1016/j.molcel.2015.05.035
ISSN1097-4164
AutoresTessa E. F. Quax, Nico J. Claassens, Dieter Söll, John van der Oost,
Tópico(s)Genomics and Phylogenetic Studies
ResumoThe redundancy of the genetic code implies that most amino acids are encoded by multiple synonymous codons. In all domains of life, a biased frequency of synonymous codons is observed at the genome level, in functionally related genes (e.g., in operons), and within single genes. Other codon bias variants include biased codon pairs and codon co-occurrence. Although translation initiation is the key step in protein synthesis, it is generally accepted that codon bias contributes to translation efficiency by tuning the elongation rate of the process. Moreover, codon bias plays an important role in controlling a multitude of cellular processes, ranging from differential protein production to protein folding. Here we review currently known types of codon bias and how they may influence translation. We discuss how understanding the principles of codon bias and translation can contribute to improved protein production and developments in synthetic biology. The redundancy of the genetic code implies that most amino acids are encoded by multiple synonymous codons. In all domains of life, a biased frequency of synonymous codons is observed at the genome level, in functionally related genes (e.g., in operons), and within single genes. Other codon bias variants include biased codon pairs and codon co-occurrence. Although translation initiation is the key step in protein synthesis, it is generally accepted that codon bias contributes to translation efficiency by tuning the elongation rate of the process. Moreover, codon bias plays an important role in controlling a multitude of cellular processes, ranging from differential protein production to protein folding. Here we review currently known types of codon bias and how they may influence translation. We discuss how understanding the principles of codon bias and translation can contribute to improved protein production and developments in synthetic biology. The central dogma of molecular biology concerns the general principle of protein expression: DNA is transcribed to mRNA, which is translated to protein. The key molecules of translation are the set of tRNAs, each providing a direct, specific link between a triplet of nucleotides and the corresponding amino acid. Ribosomes are the engines of translation that accommodate the tRNAs and mRNA (Figure 1). Deciphering the genetic code revealed that 61 codons (triplets) encode the standard 20 amino acids, whereas the remaining 3 are translation stop signals. The genetic code is nearly universal, meaning that almost all organisms use exactly the same codons for a specific amino acid. Because 18 of 20 amino acids are encoded by multiple synonymous codons, the genetic code is called “degenerate.” Because synonymous mutations do not affect the identity of the encoded amino acid, they were originally thought to have no consequences for protein function or organismal fitness and were therefore regarded as “silent mutations.” However, comparative sequence analysis revealed a non-random distribution of synonymous codons in genes of different organisms. Each organism seems to prefer a different set of codons over others; this phenomenon is called codon bias (Sharp and Li, 1986Sharp P.M. Li W.H. An evolutionary perspective on synonymous codon usage in unicellular organisms.J. Mol. Evol. 1986; 24: 28-38Crossref PubMed Scopus (637) Google Scholar). Several important variations of codon bias have recently been discovered, such as the existence of a ramp of rare, slowly translated codons at the 5′ end of protein-coding sequences (Tuller et al., 2010Tuller T. Carmi A. Vestsigian K. Navon S. Dorfan Y. Zaborske J. Pan T. Dahan O. Furman I. Pilpel Y. An evolutionarily conserved mechanism for controlling the efficiency of protein translation.Cell. 2010; 141: 344-354Abstract Full Text Full Text PDF PubMed Scopus (579) Google Scholar) and the co-occurrence of certain codons (Cannarozzi et al., 2010Cannarozzi G. Schraudolph N.N. Faty M. von Rohr P. Friberg M.T. Roth A.C. Gonnet P. Gonnet G. Barral Y. A role for codon order in translation dynamics.Cell. 2010; 141: 355-367Abstract Full Text Full Text PDF PubMed Scopus (259) Google Scholar). Apart from directly affecting general protein expression levels, it has been established that codon bias also influences protein folding (Pechmann and Frydman, 2013Pechmann S. Frydman J. Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding.Nat. Struct. Mol. Biol. 2013; 20: 237-243Crossref PubMed Scopus (309) Google Scholar) and differential regulation of protein expression (Gingold et al., 2014Gingold H. Tehler D. Christoffersen N.R. Nielsen M.M. Asmar F. Kooistra S.M. Christophersen N.S. Christensen L.L. Borre M. Sørensen K.D. et al.A dual program for translation regulation in cellular proliferation and differentiation.Cell. 2014; 158: 1281-1292Abstract Full Text Full Text PDF PubMed Scopus (283) Google Scholar). In addition to in silico analyses of codon bias, the development of ribosome density profiling has allowed experimental monitoring of the translation elongation rate at single-codon resolution (Ingolia, 2014Ingolia N.T. Ribosome profiling: new views of translation, from single codons to genome scale.Nat. Rev. Genet. 2014; 15: 205-213Crossref PubMed Scopus (415) Google Scholar). Partly on the basis of ribosome profiling data, some studies have shown that codon bias plays an important role in translation efficiency. Alternatively, however, it is concluded that translation efficiency relies on other features of the coding sequence, such as mRNA secondary structure (Kudla et al., 2009Kudla G. Murray A.W. Tollervey D. Plotkin J.B. Coding-sequence determinants of gene expression in Escherichia coli.Science. 2009; 324: 255-258Crossref PubMed Scopus (1012) Google Scholar) and the presence of Shine-Dalgarno-like sequences (Li et al., 2012Li G.-W.W. Oh E. Weissman J.S. The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria.Nature. 2012; 484: 538-541Crossref PubMed Scopus (449) Google Scholar). Here we provide a comprehensive overview of distinct variations of codon bias. We discuss how codon bias can tune expression at multiple levels: genome, operon, and gene. Furthermore, we discuss how rules for codon bias may be further elucidated and applied to improve engineering projects, ranging from the biotechnological production of single proteins to more complex synthetic biology endeavors. By the end of the 1970s, the development of DNA sequencing had enabled comparisons of the rapidly growing number of gene sequences. Striking differences were observed in the preference of distinct organisms to use certain synonymous codons over others (Grantham et al., 1980Grantham R. Gautier C. Gouy M. Mercier R. Pavé A. Codon catalog usage and the genome hypothesis.Nucleic Acids Res. 1980; 8: r49-r62PubMed Google Scholar). It did not take long to discover that codon usage also differs among genes within one genome (Ikemura, 1985Ikemura T. Codon usage and tRNA content in unicellular and multicellular organisms.Mol. Biol. Evol. 1985; 2: 13-34PubMed Google Scholar). Soon after, metrics for the frequency of optimal codons were proposed, such as the commonly used codon adaptation index (CAI) (Sharp and Li, 1987Sharp P.M. Li W.H. The codon Adaptation Index—a measure of directional synonymous codon usage bias, and its potential applications.Nucleic Acids Res. 1987; 15: 1281-1295Crossref PubMed Scopus (2645) Google Scholar). The CAI for a certain organism is based on the codon usage frequency in a reference set of highly expressed genes, such as the ones encoding ribosomal proteins. The CAI for a specific gene can be determined by comparing its codon usage frequency with this reference set. Analysis of the tRNA content of organisms in all domains of life showed that they never contain a full set of tRNAs with anticodons complementary to the 61 different codons; for example, 39 tRNAs with distinct anticodons are present in the bacterium Escherichia coli, 35 in the archaeon Sulfolobus solfataricus, and 45 in the eukaryote Homo sapiens (Table 1). In some Mycoplasma species and related species, the smallest sets are found, consisting of only 28 tRNAs with distinct anticodons (Grosjean et al., 2010Grosjean H. de Crécy-Lagard V. Marck C. Deciphering synonymous codons in the three domains of life: co-evolution with specific tRNA modification enzymes.FEBS Lett. 2010; 584: 252-264Abstract Full Text Full Text PDF PubMed Scopus (194) Google Scholar). Translation of multiple synonymous codons by a single tRNA has been demonstrated to occur by wobble base-pairing: standard Watson-Crick base-pairing (A-U, G-C) is required at the first and second positions of a codon, and “wobbling” (e.g., G-U) is allowed at the third position of a codon (corresponding to the 5′ position of the anticodon, i.e., position 34 of a tRNA) (Crick, 1966Crick F.H. Codon–anticodon pairing: the wobble hypothesis.J. Mol. Biol. 1966; 19: 548-555Crossref PubMed Scopus (1298) Google Scholar, Söll et al., 1966Söll D. Jones D.S. Ohtsuka E. Faulkner R.D. Lohrmann R. Hayatsu H. Khorana H.G. Specificity of sRNA for recognition of codons as studied by the ribosomal binding technique.J. Mol. Biol. 1966; 19: 556-573Crossref PubMed Scopus (43) Google Scholar). However, the affinity by which synonymous codons are recognized via wobble base-pairing is not similar. For instance, tRNAs with G in the 5′ position of the anticodon have a higher binding affinity for C-ending codons than for U-ending codons (Crick, 1966Crick F.H. Codon–anticodon pairing: the wobble hypothesis.J. Mol. Biol. 1966; 19: 548-555Crossref PubMed Scopus (1298) Google Scholar, Söll et al., 1966Söll D. Jones D.S. Ohtsuka E. Faulkner R.D. Lohrmann R. Hayatsu H. Khorana H.G. Specificity of sRNA for recognition of codons as studied by the ribosomal binding technique.J. Mol. Biol. 1966; 19: 556-573Crossref PubMed Scopus (43) Google Scholar).Table 1Codon Frequencies and tRNA Gene Copy Numbers for E. coli, S. solfataricus, and H. sapiensSecond Base CodonTCAGAmino AcidEcoCodon %(tRNA #)SsoCodon %(tRNA #)HsaCodon %(tRNA #)Amino AcidEcoCodon %(tRNA #)SsoCodon %(tRNA #)HsaCodon %(tRNA #)Amino AcidEcoCodon %(tRNA #)SsoCodon %(tRNA #)HsaCodon %(tRNA #)Amino AcidEcoCodon %(tRNA #)SsoCodon %(tRNA #)HsaCodon %(tRNA #)First Base CodonTPhe2.22(0)2.63(0)1.76(0)Ser0.80(0)1.50(0)1.52(11)Tyr1.60(0)3.10(0)1.22(1)Cys0.50(0)0.40(0)1.06(0)TThird Base Codon1.65(2)1.80(1)2.03(12)0.86(2)0.76(1)1.77(0)1.22(3)1.69(1)1.53(14)0.64(1)0.19(1)1.26(30)CLeu1.39(1)4.11(1)0.77(7)0.71(1)1.55(1)1.22(5)Stop0.21(0)0.18(0)0.10(2)aSuppressor tRNAs suppressing the stop codon.Stop0.09(1)btRNAs incorporating selenocysteine amino acid into selenoproteins and suppressing the TGA stop codon.0.10(3)btRNAs incorporating selenocysteine amino acid into selenoproteins and suppressing the TGA stop codon.0.16(3)btRNAs incorporating selenocysteine amino acid into selenoproteins and suppressing the TGA stop codon.A1.36(1)1.59(1)1.29(7)0.89(1)0.48(1)0.44(4)0.03(0)0.07(0)0.08(1)aSuppressor tRNAs suppressing the stop codon.Trp1.53(1)1.05(1)1.32(9)GC1.10(0)1.52(0)1.32(12)Pro0.70(0)1.23(0)1.75(10)His1.29(0)0.82(0)1.09(0)Arg2.09(4)0.17(0)0.45(7)T1.11(1)0.70(1)1.96(0)0.55(1)0.55(1)1.98(0)0.97(1)0.46(1)1.51(11)2.2(0)0.06(1)1.04(0)C0.39(1)1.91(1)0.72(3)0.84(1)1.60(1)1.69(7)Gln1.54(2)1.56(1)1.23(11)0.35(0)0.13(1)0.62(6)A5.29(4)0.55(1)3.96(10)2.32(1)0.41(1)0.69(4)2.89(2)0.53(1)3.42(20)0.54(1)0.05(1)1.14(4)GAIle3.04(0)3.36(0)1.60(14)Thr0.89(0)2.01(0)1.31(10)Asn1.77(0)2.63(0)1.70(2)Ser0.87(0)1.66(0)1.21(0)T2.52(3)1.11(1)2.08(3)2.34(2)0.68(1)1.89(0)2.16(4)1.67(1)1.91(32)1.60(1)0.73(1)1.95(8)C0.43(0)4.94(0)0.75(5)0.70(1)1.38(1)1.51(6)Lys3.37(6)3.96(1)2.44(16)Arg0.20(1)2.52(1)1.22(6)AMet Start2.78(8)2.07(3)2.20(20)1.44(2)0.64(1)0.61(6)1.03(0)3.76(1)3.19(17)0.11(1)1.76(1)1.20(5)GGVal1.83(0)2.76(0)1.10(11)Ala1.53(0)2.24(0)1.84(29)Asp3.22(0)3.42(0)2.18(0)Gly2.48(0)2.19(0)1.08(0)T1.53(2)0.72(1)1.45(0)2.56(2)0.72(1)2.77(0)1.91(3)1.25(1)2.51(19)2.97(4)0.67(1)2.22(15)C1.09(5)2.81(1)0.71(5)2.02(3)1.92(1)1.58(9)Glu3.96(4)3.84(1)2.90(13)0.79(1)2.58(1)1.65(9)A2.62(0)1.22(1)2.18(16)3.37(0)0.71(1)0.74(5)1.78(0)2.95(1)3.96(13)1.11(1)0.97(1)1.65(7)GOverview of codon frequency usage in all coding sequences and tRNA gene copy numbers for E. coli K12 (Eco), S. solfataricus (Sso), and H. sapiens (Hsa) (data obtained from http://gtrnadb.ucsc.edu).a Suppressor tRNAs suppressing the stop codon.b tRNAs incorporating selenocysteine amino acid into selenoproteins and suppressing the TGA stop codon. Open table in a new tab Overview of codon frequency usage in all coding sequences and tRNA gene copy numbers for E. coli K12 (Eco), S. solfataricus (Sso), and H. sapiens (Hsa) (data obtained from http://gtrnadb.ucsc.edu). The influence of wobble base-pairing on decoding rates of codons by the ribosome is still unresolved and complex to analyze. The translation kinetics of different codon-anticodon pairs are complex, as the following processes can play a role: (1) the diffusion kinetics of the matching tRNA, (2) the relative codon-binding affinity of matching tRNAs over mismatching tRNAs (Gromadski et al., 2006Gromadski K.B. Daviter T. Rodnina M.V. A uniform response to mismatches in codon-anticodon complexes ensures ribosomal fidelity.Mol. Cell. 2006; 21: 369-377Abstract Full Text Full Text PDF PubMed Scopus (113) Google Scholar), and (3) the translocation kinetics of mRNA and tRNA through the ribosome, which are affected by anticodon-codon interactions (Khade and Joseph, 2011Khade P.K. Joseph S. Messenger RNA interactions in the decoding center control the rate of translocation.Nat. Struct. Mol. Biol. 2011; 18: 1300-1302Crossref PubMed Scopus (32) Google Scholar). Recently conflicting results were published that reported either slower (Stadler and Fire, 2011Stadler M. Fire A. Wobble base-pairing slows in vivo translation elongation in metazoans.RNA. 2011; 17: 2063-2073Crossref PubMed Scopus (128) Google Scholar) or faster (Gardin et al., 2014Gardin J. Yeasmin R. Yurovsky A. Cai Y. Skiena S. Futcher B. Measurement of average decoding rates of the 61 sense codons in vivo.eLife. 2014; 3: 1-20Crossref Scopus (132) Google Scholar) decoding of wobbling codons. This process deserves a more detailed analysis of both data sets and methods and should also take into account the effect of tRNA modifications on wobble base-pairing. Modified nucleotides present in tRNAs further extend the range of recognized synonymous codons by affecting the ability of these tRNAs to form wobble base pairs (Agris et al., 2007Agris P.F. Vendeix F.A.P. Graham W.D. tRNA’s wobble decoding of the genome: 40 years of modification.J. Mol. Biol. 2007; 366: 1-13Crossref PubMed Scopus (404) Google Scholar). Some specific, key tRNA modifications are present in only some domains of life (Grosjean et al., 2010Grosjean H. de Crécy-Lagard V. Marck C. Deciphering synonymous codons in the three domains of life: co-evolution with specific tRNA modification enzymes.FEBS Lett. 2010; 584: 252-264Abstract Full Text Full Text PDF PubMed Scopus (194) Google Scholar). First, a key tRNA modification, present in eukaryotes and to some extent in bacteria, is the modification of adenine-34 to inosine-34, which allows non-Watson-Crick pairing with adenine, cytosine, and uridine. Second, exclusively in bacteria, the key tRNA modifications of uridine-34 to hydroxy-uridine and derivatives are found, allowing wobble pairing with adenine, guanosine, and uridine. These key tRNA modifications explain many differences in the tRNA sets that are present in archaea, bacteria, and eukaryotes (Table 1) (Novoa et al., 2012Novoa E.M. Pavon-Eternod M. Pan T. Ribas de Pouplana L. A role for tRNA modifications in genome structure and codon usage.Cell. 2012; 149: 202-213Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar). After the discovery of codon bias, a positive correlation was found between the frequency of codons and the concentration of tRNAs with complementary anticodons (Figure 2A) (Ikemura, 1985Ikemura T. Codon usage and tRNA content in unicellular and multicellular organisms.Mol. Biol. Evol. 1985; 2: 13-34PubMed Google Scholar). This fact was established for several prokaryotes and unicellular eukaryotes (Kanaya et al., 1999Kanaya S. Yamada Y. Kudo Y. Ikemura T. Studies of codon usage and tRNA genes of 18 unicellular organisms and quantification of Bacillus subtilis tRNAs: gene expression level and species-specific diversity of codon usage based on multivariate analysis.Gene. 1999; 238: 143-155Crossref PubMed Scopus (333) Google Scholar). However, this correlation initially could not be identified for several, mostly multicellular eukaryotes. To better analyze the relation between codon frequency bias and tRNA abundance in multicellular organisms, the tRNA adaptation index (tAI) was developed (dos Reis et al., 2004dos Reis M. Savva R. Wernisch L. Solving the riddle of codon usage preferences: a test for translational selection.Nucleic Acids Res. 2004; 32: 5036-5044Crossref PubMed Scopus (468) Google Scholar). This metric is based on the copy number of tRNA genes, assumed to be correlated with tRNA abundance in cells, and also takes into account the efficiency of codon-anticodon binding, related to Crick’s wobble rules (Crick, 1966Crick F.H. Codon–anticodon pairing: the wobble hypothesis.J. Mol. Biol. 1966; 19: 548-555Crossref PubMed Scopus (1298) Google Scholar). On the basis of computational analyses, it was concluded that organisms with larger genomes have higher tRNA gene redundancy, which would decrease selection for specific codons (dos Reis et al., 2004dos Reis M. Savva R. Wernisch L. Solving the riddle of codon usage preferences: a test for translational selection.Nucleic Acids Res. 2004; 32: 5036-5044Crossref PubMed Scopus (468) Google Scholar). This explained why in multicellular organisms with larger genomes, no positive correlation between codon usage and tRNA abundance could be identified in many studies. However, most studies at that time estimated tRNA abundance on the basis of tRNA gene copy numbers. Correlations based on tRNA copy numbers do not take into account that pools of distinct tRNAs and aminoacyl-tRNA species are dynamic and can vary considerably in different conditions. For example, it was demonstrated by microarray analysis that tRNA expression abundance in humans varies widely among different tissues. This abundance could be statistically correlated to codon usage of highly expressed genes specific for those tissues (Dittmar et al., 2006Dittmar K.A. Goodenbour J.M. Pan T. Tissue-specific differences in human transfer RNA expression.PLoS Genet. 2006; 2: e221Crossref PubMed Scopus (404) Google Scholar). Furthermore, in bacteria, it was found that the charging levels of different tRNAs recognizing synonymous codons vary drastically in response to amino acid starvation; that is, although the pool of some synonymous tRNAs remains completely charged, the charged fraction of others can decline to zero (Dittmar et al., 2005Dittmar K.A. Sørensen M.A. Elf J. Ehrenberg M. Pan T. Sørensen M. Selective charging of tRNA isoacceptors induced by amino-acid starvation.EMBO Rep. 2005; 6: 151-157Crossref PubMed Scopus (159) Google Scholar, Elf et al., 2003Elf J. Nilsson D. Tenson T. Ehrenberg M. Selective charging of tRNA isoacceptors explains patterns of codon usage.Science. 2003; 300: 1718-1722Crossref PubMed Scopus (191) Google Scholar). So far, codon frequencies were correlated mostly with the total supply of tRNAs. However, when a more frequently used codon is recognized by a more abundant tRNA species, this codon will also compete for this tRNA with more codons. To take this into account, the normalized translation efficiency (nTE) metric was introduced, correcting for supply as well as demand rates of tRNAs (Pechmann and Frydman, 2013Pechmann S. Frydman J. Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding.Nat. Struct. Mol. Biol. 2013; 20: 237-243Crossref PubMed Scopus (309) Google Scholar). The nTE considers codons to be more optimal if their relative tRNA abundance on the basis of gene copy number (supply) exceeds their relative cognate codon usage (demand) on the basis of codon frequencies in mRNA. Although the tAI and nTE already give good indications of the availability of tRNAs for the translation of synonymous codons, this approximation could still be improved. The actual important value is the level of mature aminoacyl-tRNAs ready for amino acid delivery in the translation process. However, because the levels of tRNA expression and charging can undergo major fluctuations on the basis of cellular condition, it is not straightforward to take these values into account. In addition, it was demonstrated that codon frequency bias can be better correlated with tRNA gene frequencies in all domains of life, if two major, domain-specific tRNA modification types are taken into account (Novoa et al., 2012Novoa E.M. Pavon-Eternod M. Pan T. Ribas de Pouplana L. A role for tRNA modifications in genome structure and codon usage.Cell. 2012; 149: 202-213Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar). To summarize, highly expressed proteins are generally encoded by genes that contain relatively high proportions of codons recognized by abundant, charged tRNAs with kinetically efficient codon-anticodon base-pairing. This explains to a large extent the observed codon frequency bias in many genes and genomes. In addition to codon frequency bias, two other general types of codon bias have been identified in recent years: codon pair bias and codon co-occurrence bias; these will be discussed hereafter. Recently it was shown that not only the overall frequency of synonymous codons but also the order in which they reside in a gene are biased. While studying all coding sequences of Saccharomyces cerevisiae, a bias was revealed of clustered synonymous codons, called codon co-occurrence bias. Instead of a random distribution of synonymous codons on a coding sequence, there is a bias to cluster those synonymous codons that are recognized by the same tRNA (i.e., identical codons and isoaccepting codons) (Cannarozzi et al., 2010Cannarozzi G. Schraudolph N.N. Faty M. von Rohr P. Friberg M.T. Roth A.C. Gonnet P. Gonnet G. Barral Y. A role for codon order in translation dynamics.Cell. 2010; 141: 355-367Abstract Full Text Full Text PDF PubMed Scopus (259) Google Scholar) (Figure 2B). The effect of co-occurrence bias involves both frequent and rare codons and is most prominent in highly expressed genes that must be rapidly induced, such as those involved in stress response (Cannarozzi et al., 2010Cannarozzi G. Schraudolph N.N. Faty M. von Rohr P. Friberg M.T. Roth A.C. Gonnet P. Gonnet G. Barral Y. A role for codon order in translation dynamics.Cell. 2010; 141: 355-367Abstract Full Text Full Text PDF PubMed Scopus (259) Google Scholar). It has been suggested that tRNAs remain in proximity to the translating ribosome after their exit from the E site and that they are subsequently recharged by the corresponding aminoacyl-tRNA synthetases that somehow co-localize with the ribosome (Cannarozzi et al., 2010Cannarozzi G. Schraudolph N.N. Faty M. von Rohr P. Friberg M.T. Roth A.C. Gonnet P. Gonnet G. Barral Y. A role for codon order in translation dynamics.Cell. 2010; 141: 355-367Abstract Full Text Full Text PDF PubMed Scopus (259) Google Scholar, Godinic-Mikulcic et al., 2014Godinic-Mikulcic V. Jaric J. Greber B.J. Franke V. Hodnik V. Anderluh G. Ban N. Weygand-Durasevic I. Archaeal aminoacyl-tRNA synthetases interact with the ribosome to recycle tRNAs.Nucleic Acids Res. 2014; 42: 5191-5201Crossref PubMed Scopus (17) Google Scholar). At the next occurrence of the same or isoaccepting codon, the charged tRNA would be readily available for translation; this would have a positive effect on translation efficiency (Cannarozzi et al., 2010Cannarozzi G. Schraudolph N.N. Faty M. von Rohr P. Friberg M.T. Roth A.C. Gonnet P. Gonnet G. Barral Y. A role for codon order in translation dynamics.Cell. 2010; 141: 355-367Abstract Full Text Full Text PDF PubMed Scopus (259) Google Scholar). Co-occurrence bias has been demonstrated in eukaryotes, bacteria, and archaea (Cannarozzi et al., 2010Cannarozzi G. Schraudolph N.N. Faty M. von Rohr P. Friberg M.T. Roth A.C. Gonnet P. Gonnet G. Barral Y. A role for codon order in translation dynamics.Cell. 2010; 141: 355-367Abstract Full Text Full Text PDF PubMed Scopus (259) Google Scholar, Shao et al., 2012Shao Z.Q. Zhang Y.M. Feng X.Y. Wang B. Chen J.Q. Synonymous codon ordering: a subtle but prevalent strategy of bacteria to improve translational efficiency.PLoS ONE. 2012; 7: e33547Crossref PubMed Scopus (24) Google Scholar, Zhang et al., 2013Zhang Y.M. Shao Z.Q. Yang L.T. Sun X.Q. Mao Y.F. Chen J.Q. Wang B. Non-random arrangement of synonymous codons in archaea coding sequences.Genomics. 2013; 101: 362-367Crossref PubMed Scopus (10) Google Scholar). However, co-occurrence of identical codons is strongly biased in all domains of life, whereas co-occurrence of non-identical isoaccepting codons is less prominent in prokaryotes than in eukaryotes (Shao et al., 2012Shao Z.Q. Zhang Y.M. Feng X.Y. Wang B. Chen J.Q. Synonymous codon ordering: a subtle but prevalent strategy of bacteria to improve translational efficiency.PLoS ONE. 2012; 7: e33547Crossref PubMed Scopus (24) Google Scholar, Zhang et al., 2013Zhang Y.M. Shao Z.Q. Yang L.T. Sun X.Q. Mao Y.F. Chen J.Q. Wang B. Non-random arrangement of synonymous codons in archaea coding sequences.Genomics. 2013; 101: 362-367Crossref PubMed Scopus (10) Google Scholar). The fact that co-occurrence of non-identical isoaccepting occurs more in eukaryotes most likely correlates with differences in affinity of codon-anticodon pairs between eukaryotes and prokaryotes (Shao et al., 2012Shao Z.Q. Zhang Y.M. Feng X.Y. Wang B. Chen J.Q. Synonymous codon ordering: a subtle but prevalent strategy of bacteria to improve translational efficiency.PLoS ONE. 2012; 7: e33547Crossref PubMed Scopus (24) Google Scholar). Domain-specific key modifications of tRNA result in differences in affinities of wobble base-pairing for certain synonymous codons. It has been hypothesized that only non-identical codon pairs that are recognized by a tRNA with similarly high affinity may result in co-occurrence bias (Shao et al., 2012Shao Z.Q. Zhang Y.M. Feng X.Y. Wang B. Chen J.Q. Synonymous codon ordering: a subtle but prevalent strategy of bacteria to improve translational efficiency.PLoS ONE. 2012; 7: e33547Crossref PubMed Scopus (24) Google Scholar). The described findings demonstrate that the use of identical and some isoaccepting codons in close proximity are generally advantageous for the translation process. In addition to codon frequency and co-occurrence, also the context in which a codon resides is under selective constraint. Nucleotides neighboring a particular codon are distributed in a non-random manner (Buchan et al., 2006Buchan J.R. Aucott L.S. Stansfield I. tRNA properties help shape codon pair preferences in open reading frames.Nucleic Acids Res. 2006; 34: 1015-1027Crossref PubMed Scopus (81) Google Scholar, Gutman and Hatfield, 1989Gutman G.A. Hatfield G.W. Nonrandom utilization of codon pairs in Escherichia coli.Proc. Natl. Acad. Sci. U S A. 1989; 86: 3699-3703Crossref PubMed Scopus (204) Google Scholar). This phenomenon is called codon pair bias (Figure 2C). For example, there are eight possible codon pairs to encode the adjacent amino acids alanine and glutamate. On the basis of codon frequencies, one would expect these amino acids to be encoded equally by GCC-GAA and GCA-GAG codon pairs. However, in humans, the GCC-GAA pair is heavily underrepresented compared with the expected frequency, even though it contains GCC, the most prevalent codon for alanine (Coleman et al., 2008Coleman J.R. Papamichail D. Skiena S. Futcher B. Wimmer E. Mueller S. Virus attenuation by genome-scale changes in codon pair bias.Science. 2008; 320: 1784-1787Crossref PubMed Scopus (470) Google Scholar). Some codon pairs are universally avoided or preferred; for example, nnUAnn codon pairs are usually underrepresented, whereas nnGCnn codon pairs are most preferred (Tats et al., 2008Tats A. Tenson T. Remm M. Preferred and avoided codon pairs in three domains of life.BMC Genomics. 2008; 9: 463Crossref PubMed Scopus (75) Google Scholar). Although the exact mechanism by which codon pair bias might enhance translation efficiency is currently not well understood, it is assumed that tRNAs in the A and P sites of the ribosome can interact and as such influence the efficiency of the translation process (Figure 2C) (Buchan et al., 2006Buchan J.R. Aucott L.S. Stansfield I. tRNA properties help shape codon pair preferences in open reading frames.Nucleic Acids Res. 2006; 34: 1015-1027Crossref PubMed Scopus (81) Google Scholar). Several viral genomes also contain codon pair bias, which generally matches that of their host. Modification of this codon pair usage in virulence-related genes of viruses and has been presented as an elegant strategy to produce vaccines with attenuated viruses (Coleman et al., 2008Coleman J.R. Papamichail D. Skiena S. Futcher B. Wimmer E. Mueller S. Virus attenuation by genome-scale changes in codon pair bias.Science. 2008; 320: 1784-1787Crossref PubMed Scopus (470) Google Scholar). However, it was recently suggested that this attenuation may be caused by an increased CpG and UpA dinucleotide bias rather than by a changed codon pair bias, because these dinucleotides are generally used at a low frequency in RNA and small DNA viruses infecting mammals and plants (Tulloch et al., 2014Tulloch F. Atkinson N.J. Evans D.J. Ryan M.D. Simmonds P. RNA virus attenuation by codon pair
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