The frequency of translational misreading errors in E. coli is largely determined by tRNA competition

The tri-frame model

The tri-frame model gives mathematical expression to the transcription and translation processes, and considers all three reading frames (RFs). RNA polymerases transcribe DNA in single nucleotide increments, but ribosomes translate mRNA in pairings of three (triplets or codons). The set of triplets in the mRNA, starting with the initiation codon (usually AUG) defines the open reading frame (ORF). Since ribosomes do not always translocate three nucleotide positions, two additional RFs are accessible. The -1 RF and the +1 RF are triplet pairings of the mRNA, which are accessed by shifting one nucleotide position in the 5' and 3' directions, respectively. Transcription is modeled as a linear operator that maps the initial codons in all three frames into other codon sets to account for possible transcriptional errors. Translational errors (missense errors) originate from misacylation of tRNAs and misreading of aa-tRNAs by the ribosome. Translation is modeled as a linear mapping from codons into aa-tRNA species, which includes misreading errors. A final transformation from aa-tRNA species into amino acids provides the probability distributions of possible amino acids into which the codons in all three frames could be translated. An important element of the tri-frame model is the ribosomal occupancy probability. It is a vector in R(3) that gives the probability to find the ribosome in the ORF, -1 or +1 RF at each codon position. The sequence of vectors, from the first to the final codon position, gives a history of ribosome frameshifting. The model is powerful: it provides explicit expressions for (1) yield of error-free protein, (2) fraction of prematurely terminated polypeptides, (3) number of transcription errors, (4) number of translation errors and (5) mutations due to frameshifting. The theory is demonstrated for the three genes rpsU, dnaG and rpoD of Escherichia coli, which lie on the same operon, as well as for the prfB gene.

Codon-triplet context unveils unique features of the Candida albicans protein coding genome

Background

The evolutionary forces that determine the arrangement of synonymous codons within open reading frames and fine tune mRNA translation efficiency are not yet understood. In order to tackle this question we have carried out a large scale study of codon-triplet contexts in 11 fungal species to unravel associations or relationships between codons present at the ribosome A-, P- and E-sites during each decoding cycle.

Results

Our analysis unveiled high bias within the context of codon-triplets, in particular strong preference for triplets of identical codons. We have also identified a surprisingly large number of codon-triplet combinations that vanished from fungal ORFeomes. Candida albicans exacerbated these features, showed an unbalanced tRNA population for decoding its pool of codons and used near-cognate decoding for a large set of codons, suggesting that unique evolutionary forces shaped the evolution of its ORFeome.

Conclusion

We have developed bioinformatics tools for large-scale analysis of codon-triplet contexts. These algorithms identified codon-triplets context biases, allowed for large scale comparative codon-triplet analysis, and identified rules governing codon-triplet context. They could also detect alterations to the standard genetic code.

Evolution of the genetic code: partial optimization of a random code for robustness to translation error in a rugged fitness landscape

Background

The standard genetic code table has a distinctly non-random structure, with similar amino acids often encoded by codons series that differ by a single nucleotide substitution, typically, in the third or the first position of the codon. It has been repeatedly argued that this structure of the code results from selective optimization for robustness to translation errors such that translational misreading has the minimal adverse effect. Indeed, it has been shown in several studies that the standard code is more robust than a substantial majority of random codes. However, it remains unclear how much evolution the standard code underwent, what is the level of optimization, and what is the likely starting point.

Results

We explored possible evolutionary trajectories of the genetic code within a limited domain of the vast space of possible codes. Only those codes were analyzed for robustness to translation error that possess the same block structure and the same degree of degeneracy as the standard code. This choice of a small part of the vast space of possible codes is based on the notion that the block structure of the standard code is a consequence of the structure of the complex between the cognate tRNA and the codon in mRNA where the third base of the codon plays a minimum role as a specificity determinant. Within this part of the fitness landscape, a simple evolutionary algorithm, with elementary evolutionary steps comprising swaps of four-codon or two-codon series, was employed to investigate the optimization of codes for the maximum attainable robustness. The properties of the standard code were compared to the properties of four sets of codes, namely, purely random codes, random codes that are more robust than the standard code, and two sets of codes that resulted from optimization of the first two sets. The comparison of these sets of codes with the standard code and its locally optimized version showed that, on average, optimization of random codes yielded evolutionary trajectories that converged at the same level of robustness to translation errors as the optimization path of the standard code; however, the standard code required considerably fewer steps to reach that level than an average random code. When evolution starts from random codes whose fitness is comparable to that of the standard code, they typically reach much higher level of optimization than the standard code, i.e., the standard code is much closer to its local minimum (fitness peak) than most of the random codes with similar levels of robustness. Thus, the standard genetic code appears to be a point on an evolutionary trajectory from a random point (code) about half the way to the summit of the local peak. The fitness landscape of code evolution appears to be extremely rugged, containing numerous peaks with a broad distribution of heights, and the standard code is relatively unremarkable, being located on the slope of a moderate-height peak.

Conclusion

The standard code appears to be the result of partial optimization of a random code for robustness to errors of translation. The reason the code is not fully optimized could be the trade-off between the beneficial effect of increasing robustness to translation errors and the deleterious effect of codon series reassignment that becomes increasingly severe with growing complexity of the evolving system. Thus, evolution of the code can be represented as a combination of adaptation and frozen accident.

Reviewers

This article was reviewed by David Ardell, Allan Drummond (nominated by Laura Landweber), and Rob Knight.

Open Peer Review

This article was reviewed by David Ardell, Allan Drummond (nominated by Laura Landweber), and Rob Knight.

Mutational analysis of S12 protein and implications for the accuracy of decoding by the ribosome

The fidelity of aminoacyl-tRNA selection by the ribosome depends on a conformational switch in the decoding center of the small ribosomal subunit induced by cognate but not by near-cognate aminoacyl-tRNA. The aminoglycosides paromomycin and streptomycin bind to the decoding center and induce related structural rearrangements that explain their observed effects on miscoding. Structural and biochemical studies have identified ribosomal protein S12 (as well as specific nucleotides in 16S ribosomal RNA) as a critical molecular contributor in distinguishing between cognate and near-cognate tRNA species as well as in promoting more global rearrangements in the small subunit, referred to as "closure." Here we use a mutational approach to define contributions made by two highly conserved loops in S12 to the process of tRNA selection. Most S12 variant ribosomes tested display increased levels of fidelity (a "restrictive" phenotype). Interestingly, several variants, K42A and R53A, were substantially resistant to the miscoding effects of paromomycin. Further characterization of the compromised paromomycin response identified a probable second, fidelity-modulating binding site for paromomycin in the 16S ribosomal RNA that facilitates closure of the small subunit and compensates for defects associated with the S12 mutations.

Large scale comparative codon-pair context analysis unveils general rules that fine-tune evolution of mRNA primary structure

Background

Codon usage and codon-pair context are important gene primary structure features that influence mRNA decoding fidelity. In order to identify general rules that shape codon-pair context and minimize mRNA decoding error, we have carried out a large scale comparative codon-pair context analysis of 119 fully sequenced genomes.

Methodologies/Principal Findings

We have developed mathematical and software tools for large scale comparative codon-pair context analysis. These methodologies unveiled general and species specific codon-pair context rules that govern evolution of mRNAs in the 3 domains of life. We show that evolution of bacterial and archeal mRNA primary structure is mainly dependent on constraints imposed by the translational machinery, while in eukaryotes DNA methylation and tri-nucleotide repeats impose strong biases on codon-pair context.

Conclusions

The data highlight fundamental differences between prokaryotic and eukaryotic mRNA decoding rules, which are partially independent of codon usage.

Ribosome kinetics and aa-tRNA competition determine rate and fidelity of peptide synthesis

It is generally accepted that the translation rate depends on the availability of cognate aa-tRNAs. In this study it is shown that the key factor that determines translation rate is the competition between near-cognate and cognate aa-tRNAs. The transport mechanism in the cytoplasm is diffusion, thus the competition between cognate, near-cognate and non-cognate aa-tRNAs to bind to the ribosome is a stochastic process. Two competition measures are introduced; C(i) and R(i) (i=1, 64) are quotients of the arrival frequencies of near-cognates vs. cognates and non-cognates vs. cognates, respectively. Furthermore, the reaction rates of bound cognates differ from those of bound near-cognates. If a near-cognate aa-tRNA binds to the A site of the ribosome, it may be rejected at the anti-codon recognition step or proofreading step or it may be accepted. Regardless of its fate, the near-cognates and non-cognates have caused delays of varying duration to the observed rate of translation. Rate constants have been measured at a temperature of 20 degrees C by (Gromadski, K.B., Rodnina, M.V., 2004. Kinetic determinants of high-fidelity tRNA discrimination on the ribosome. Mol. Cell 13, 191-200). These rate constants have been re-evaluated at 37 degrees C, using experimental data at 24.5 degrees C and 37 degrees C (Varenne, S., et al., 1984. Translation in a non-uniform process: effect of tRNA availability on the rate of elongation of nascent polypeptide chains. J. Mol. Biol. 180, 549-576). The key results of the study are: (i) the average time (at 37 degrees C) to add an amino acid, as defined by the ith codon, to the nascent peptide chain is: tau(i)=9.06+1.445x[10.48C(i)+0.5R(i)] (in ms); (ii) the misreading frequency is directly proportional to the near-cognate competition, E(i)=0.0009C(i); (iii) the competition from near-cognates, and not the availability of cognate aa-tRNAs, is the most important factor that determines the translation rate - the four codons with highest near-cognate competition (in the case of E. coli) are [GCC]>[CGG]>[AGG]>[GGA], which overlap only partially with the rarest codons: [AGG]<[CCA]<[GCC]<[CAC]; (iv) based on the kinetic rates at 37 degrees C, the average time to insert a cognate amino acid is 9.06ms and the average delay to process a near-cognate aa-tRNA is 10.45ms and (vii) the model also provides estimates of the vacancy times of the A site of the ribosome - an important factor in frameshifting.

Evolved orthogonal ribosomes enhance the efficiency of synthetic genetic code expansion

In vivo incorporation of unnatural amino acids by amber codon suppression is limited by release factor-1-mediated peptide chain termination. Orthogonal ribosome-mRNA pairs function in parallel with, but independent of, natural ribosomes and mRNAs. Here we show that an evolved orthogonal ribosome (ribo-X) improves tRNA(CUA)-dependent decoding of amber codons placed in orthogonal mRNA. By combining ribo-X, orthogonal mRNAs and orthogonal aminoacyl-tRNA synthetase/tRNA pairs in Escherichia coli, we increase the efficiency of site-specific unnatural amino acid incorporation from approximately 20% to >60% on a single amber codon and from <1% to >20% on two amber codons. We hypothesize that these increases result from a decreased functional interaction of the orthogonal ribosome with release factor-1. This technology should minimize the functional and phenotypic effects of truncated proteins in experiments that use unnatural amino acid incorporation to probe protein function in vivo.

Differentiating between near- and non-cognate codons in Saccharomyces cerevisiae

Background

Decoding of mRNAs is performed by aminoacyl tRNAs (aa-tRNAs). This process is highly accurate, however, at low frequencies (10⁻³ – 10⁻⁴) the wrong aa-tRNA can be selected, leading to incorporation of aberrant amino acids. Although our understanding of what constitutes the correct or cognate aa-tRNA:mRNA interaction is well defined, a functional distinction between near-cognate or single mismatched, and unpaired or non-cognate interactions is lacking.

Methodology/Principal Findings

Misreading of several synonymous codon substitutions at the catalytic site of firefly luciferase was assayed in Saccharomyces cerevisiae. Analysis of the results in the context of current kinetic and biophysical models of aa-tRNA selection suggests that the defining feature of near-cognate aa-tRNAs is their potential to form mini-helical structures with A-site codons, enabling stimulation of GTPase activity of eukaryotic Elongation Factor 1A (eEF1A). Paromomycin specifically stimulated misreading of near-cognate but not of non-cognate aa-tRNAs, providing a functional probe to distinguish between these two classes. Deletion of the accessory elongation factor eEF1Bγ promoted increased misreading of near-cognate, but hyperaccurate reading of non-cognate codons, suggesting that this factor also has a role in tRNA discrimination. A mutant of eEF1Bα, the nucleotide exchange factor for eEF1A, promoted a general increase in fidelity, suggesting that the decreased rates of elongation may provide more time for discrimination between aa-tRNAs. A mutant form of ribosomal protein L5 promoted hyperaccurate decoding of both types of codons, even though it is topologically distant from the decoding center.

Conclusions/Signficance

It is important to distinguish between near-cognate and non-cognate mRNA:tRNA interactions, because such a definition may be important for informing therapeutic strategies for suppressing these two different categories of mutations underlying many human diseases. This study suggests that the defining feature of near-cognate aa-tRNAs is their potential to form mini-helical structures with A-site codons in the ribosomal decoding center. An aminoglycoside and a ribosomal factor can be used to distinguish between near-cognate and non-cognate interactions.