Genetic code origin and the strength of natural selection

The time of appearance of the genetic code

I support the hypothesis that the origin of the genetic code occurred simultaneously with the evolution of cellularity. That is to say, I favour the hypothesis that the origin of the genetic code is a very, very late event in the history of life on Earth. I corroborate this hypothesis with observations favouring the progenote's stage for the Last Universal Common Ancestor (LUCA), for the ancestor of bacteria and that of archaea. Indeed, these progenotic stages would imply that - at that time - the origin of the genetic code was still ongoing simply because this origin would fall within the very definition of progenote. Therefore, if the evolution of cellularity had truly been coeval with the origin of the genetic code - at least in its terminal part - then this would favour theories such as the coevolution theory of the origin of the genetic code because this theory would postulate that this origin must have occurred in extremely complex protocellular conditions and not concerning stereochemical or physicochemical interactions having to do with other stages of the origin of life. In this sense, the coevolution theory would be corroborated while the stereochemical and physicochemical theories would be damaged. Therefore, the origin of the genetic code would be linked to the origin of the cell and not to the origin of life as sometimes asserted. Therefore, I will discuss the late hypothesis of the origin of the genetic code in the context of the theories proposed to explain this origin and more generally of its implications for the early evolution of life.

The error minimization of the genetic code would have been determined by natural selection and not by a neutral evolution

I discuss the mechanisms by which the error minimization observed in the genetic code would have been produced; that is, the ability of the genetic code to buffer, for example, the deleterious effects of translation errors. Here, I analyse whether the error minimization was produced by the intervention of natural selection or whether it is an emergent, that is, neutral property; in other words, whether it is a by-product of another mechanism that was structuring the genetic code. In particular, I criticize Massey's simulations (2008) - favouring the neutral hypothesis - which, containing elements of natural selection, would render his conclusions at least partly tautological. Furthermore, I criticize some of Koonin's (2017) interpretations regarding Massey's simulations. Finally, I criticize the opinion of Janzen et al. (2022) according to which their self-aminoacylating ribozyme system would have been capable of generating an error minimization of the genetic code as its emergent property. That is to say, I criticize, more generally, a neutral origin of error minimization. Indeed, any mechanism for structuring the genetic code would be capable of generating, in theory, such an emergent property. The problem is that to demonstrate this, it would be necessary to show that the level of optimization achieved by the genetic code would be that expected under the neutral hypothesis, the one that Janzen et al. (2022) instead they did not make. Therefore, their view is only a hypothesis and is very far from being corroborated by their results. Instead, in the literature there is a strong evidence that the level of optimization achieved by the genetic code is so high that it would imply, per se, an intervention of natural selection in the origin of error minimization of the genetic code. On the other hand, this level of optimization would be very far from what might have been produced by a neutral process.

On the organizational dynamics of the genetic code

The organization of the canonical genetic code needs to be thoroughly illuminated. Here we reorder the four nucleotides—adenine, thymine, guanine and cytosine—according to their emergence in evolution, and apply the organizational rules to devising an algebraic representation for the canonical genetic code. Under a framework of the devised code, we quantify codon and amino acid usages from a large collection of 917 prokaryotic genome sequences, and associate the usages with its intrinsic structure and classification schemes as well as amino acid physicochemical properties. Our results show that the algebraic representation of the code is structurally equivalent to a content-centric organization of the code and that codon and amino acid usages under different classification schemes were correlated closely with GC content, implying a set of rules governing composition dynamics across a wide variety of prokaryotic genome sequences. These results also indicate that codons and amino acids are not randomly allocated in the code, where the six-fold degenerate codons and their amino acids have important balancing roles for error minimization. Therefore, the content-centric code is of great usefulness in deciphering its hitherto unknown regularities as well as the dynamics of nucleotide, codon, and amino acid compositions.

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.

Solvent accessibility, residue charge and residue volume, the three ingredients of a robust amino acid substitution matrix

Cost measure matrices or different amino acid indices have been widely used for studies in many fields of biology. One major criticism of these studies might be based on the unavailability of an unbiased and yet effective amino acid substitution matrix. Throughout this study we have devised a cost measure matrix based on the solvent accessibility, residue charge, and residue volume indices. Performed analyses on this novel substitution matrix (i.e. solvent accessibility charge volume (SCV) matrix) support the uncontaminated nature of this matrix regarding the genetic code. Although highly similar to a number of previously available cost measure matrices, the SCV matrix results in a more significant optimality in the error-buffering capacity of the genetic code when compared to many other amino acid substitution matrices. Besides, a method to compare an SCV-based scoring matrix with a number of widely used matrices has been devised, the results of which highlights the robustness of this matrix in protein family discrimination.

The case for an error minimizing set of coding amino acids

The fidelity of the translation machinery largely depends on the accuracy by which the tRNAs within the living cells are charged. Aminoacyl-tRNA synthetases (aaRSs) attach amino acids to their cognate tRNAs ensuring the fidelity of translation in coding sequences. Based on the sequence analysis and catalytic domain structure, these enzymes are classified into two major groups of 10 enzymes each. In this study, we have generally tackled the role of aaRSs in decreasing the effects of mistranslations and consequently the evolution of the translation machinery. To this end, a fitness function was introduced in order to measure the accuracy by which each tRNA is charged with its cognate amino acid. Our results suggest that the aaRSs are very well optimized in "load minimization" based on their classes and their mechanisms in distinguishing the correct amino acids. Besides, our results support the idea that from an evolutionary point, a selectional pressure on the translational fidelity seems to be responsible in the occurrence of the 20 coding amino acids.

On the coevolution of genes and genetic code

The canonical genetic code acts efficiently in minimizing the effects of mistranslations and point mutations. In the work presented we have also considered the effects of single nucleotide insertions and deletions on the optimality of the genetic code. Our results suggest that the canonical genetic code compensates for the ins/del mutations as well as mistranslations and point mutations. On the other hand, we highlighted the point that ins/del mutations have a lesser impact on the selected genes of Saccharomyces cerevisiae compared to randomly generated ones. We hypothesized that the codon usage preferences in S. cerevisiae genes are responsible for the higher efficiency of translation machinery in this organism. Our results support the conjecture that codon usage preferences render the genetic code more effective in minimizing the effects of ins/del mutations.

Do universal codon-usage patterns minimize the effects of mutation and translation error?

The analysis of codon usage in nearly 900 species of the three domains of life suggests that codon usage patterns in mRNA messages do not minimize the effects of translation error.

Background

Do species use codons that reduce the impact of errors in translation or replication? The genetic code is arranged in a way that minimizes errors, defined as the sum of the differences in amino-acid properties caused by single-base changes from each codon to each other codon. However, the extent to which organisms optimize the genetic messages written in this code has been far less studied. We tested whether codon and amino-acid usages from 457 bacteria, 264 eukaryotes, and 33 archaea minimize errors compared to random usages, and whether changes in genome G+C content influence these error values.

Results

We tested the hypotheses that organisms choose their codon usage to minimize errors, and that the large observed variation in G+C content in coding sequences, but the low variation in G+U or G+A content, is due to differences in the effects of variation along these axes on the error value. Surprisingly, the biological distribution of error values has far lower variance than randomized error values, but error values of actual codon and amino-acid usages are actually greater than would be expected by chance.

Conclusion

These unexpected findings suggest that selection against translation error has not produced codon or amino-acid usages that minimize the effects of errors, and that even messages with very different nucleotide compositions somehow maintain a relatively constant error value. They raise the question: why do all known organisms use highly error-minimizing genetic codes, but fail to minimize the errors in the mRNA messages they encode?

Selection on codon usage for error minimization at the protein level

Given the structure of the genetic code, synonymous codons differ in their capacity to minimize the effects of errors due to mutation or mistranslation. I suggest that this may lead, in protein-coding genes, to a preference for codons that minimize the impact of errors at the protein level. I develop a theoretical measure of error minimization for each codon, based on amino acid similarity. This measure is used to calculate the degree of error minimization for 82 genes of Drosophila melanogaster and 432 rodent genes and to study its relationship with CG content, the degree of codon usage bias, and the rate of nucleotide substitution. I show that (i) Drosophila and rodent genes tend to prefer codons that minimize errors; (ii) this cannot be merely the effect of mutation bias; (iii) the degree of error minimization is correlated with the degree of codon usage bias; (iv) the amino acids that contribute more to codon usage bias are the ones for which synonymous codons differ more in the capacity to minimize errors; and (v) the degree of error minimization is correlated with the rate of nonsynonymous substitution. These results suggest that natural selection for error minimization at the protein level plays a role in the evolution of coding sequences in Drosophila and rodents.

The origin of the genetic code: theories and their relationships, a review

A review of the main theories proposed to explain the origin of the genetic code is presented. I analyze arguments and data in favour of different theories proposed to explain the origin of the organization of the genetic code. It is possible to suggest a mechanism that makes compatible the different theories of the origin of the code, even if these are based on a historical or physicochemical determinism and thus appear incompatible by definition. Finally, I discuss the question of why a given number of synonymous codons was attributed to the amino acids in the genetic code.

Codon Usage Bias and Mutation Constraints Reduce the Level of ErrorMinimization of the Genetic Code

Studies on the origin of the genetic code compare measures of the degree of error minimization of the standard code with measures produced by random variant codes but do not take into account codon usage, which was probably highly biased during the origin of the code. Codon usage bias could play an important role in the minimization of the chemical distances between amino acids because the importance of errors depends also on the frequency of the different codons. Here I show that when codon usage is taken into account, the degree of error minimization of the standard code may be dramatically reduced, and shifting to alternative codes often increases the degree of error minimization. This is especially true with a high CG content, which was probably the case during the origin of the code. I also show that the frequency of codes that perform better than the standard code, in terms of relative efficiency, is much higher in the neighborhood of the standard code itself, even when not considering codon usage bias; therefore alternative codes that differ only slightly from the standard code are more likely to evolve than some previous analyses suggested. My conclusions are that the standard genetic code is far from being an optimum with respect to error minimization and must have arisen for reasons other than error minimization.

Codon usage decreases the error minimization within the genetic code

The genetic code is not random but instead is organized in such a way that single nucleotide substitutions are more likely to result in changes between similar amino acids. This fidelity, or error minimization, has been proposed to be an adaptation within the genetic code. Many models have been proposed to measure this adaptation within the genetic code. However, we find that none of these consider codon usage differences between species. Furthermore, use of different indices of amino acid physicochemical characteristics leads to different estimations of this adaptation within the code. In this study, we try to establish a more accurate model to address this problem. In our model, a weighting scheme is established for mistranslation biases of the three different codon positions, transition/transversion biases, and codon usage. Different indices of amino acids' physicochemical characteristics are also considered. In contrast to pervious work, our results show that the natural genetic code is not fully optimized for error minimization. The genetic code, therefore, is not the most optimized one for error minimization, but one that balances between flexibility and fidelity for different species.

The case for an error minimizing standard genetic code

Since discovering the pattern by which amino acids are assigned to codons within the standard genetic code, investigators have explored the idea that natural selection placed biochemically similar amino acids near to one another in coding space so as to minimize the impact of mutations and/or mistranslations. The analytical evidence to support this theory has grown in sophistication and strength over the years, and counterclaims questioning its plausibility and quantitative support have yet to transcend some significant weaknesses in their approach. These weaknesses are illustrated here by means of a simple simulation model for adaptive genetic code evolution. There remain ill explored facets of the 'error minimizing' code hypothesis, however, including the mechanism and pathway by which an adaptive pattern of codon assignments emerged, the extent to which natural selection created synonym redundancy, its role in shaping the amino acid and nucleotide languages, and even the correct interpretation of the adaptive codon assignment pattern: these represent fertile areas for future research.

No accident: genetic codes freeze in error-correcting patterns of the standard genetic code

The standard genetic code poses a challenge in understanding the evolution of information processing at a fundamental level of biological organization. Genetic codes are generally coadapted with, or 'frozen' by, the protein-coding genes that they translate, and so cannot easily change by natural selection. Yet the standard code has a significantly non-random pattern that corrects common errors in the transmission of information in protein-coding genes. Because of the freezing effect and for other reasons, this pattern has been proposed not to be due to selection but rather to be incidental to other evolutionary forces or even entirely accidental. We present results from a deterministic population genetic model of code-message coevolution. We explicitly represent the freezing effect of genes on genetic codes and the perturbative effect of changes in genetic codes on genes. We incorporate characteristic patterns of mutation and translational error, namely, transition bias and positional asymmetry, respectively. Repeated selection over small successive changes produces genetic codes that are substantially, but not optimally, error correcting. In particular, our model reproduces the error-correcting patterns of the standard genetic code. Aspects of our model and results may be applicable to the general problem of adaptation to error in other natural information-processing systems.

Optimality of the genetic code with respect to protein stability and amino-acid frequencies

BACKGROUND

The genetic code is known to be efficient in limiting the effect of mistranslation errors. A misread codon often codes for the same amino acid or one with similar biochemical properties, so the structure and function of the coded protein remain relatively unaltered. Previous studies have attempted to address this question quantitatively, by estimating the fraction of randomly generated codes that do better than the genetic code in respect of overall robustness. We extended these results by investigating the role of amino-acid frequencies in the optimality of the genetic code.

RESULTS

We found that taking the amino-acid frequency into account decreases the fraction of random codes that beat the natural code. This effect is particularly pronounced when more refined measures of the amino-acid substitution cost are used than hydrophobicity. To show this, we devised a new cost function by evaluating in silico the change in folding free energy caused by all possible point mutations in a set of protein structures. With this function, which measures protein stability while being unrelated to the code's structure, we estimated that around two random codes in a billion (109) are fitter than the natural code. When alternative codes are restricted to those that interchange biosynthetically related amino acids, the genetic code appears even more optimal.

CONCLUSIONS

These results lead us to discuss the role of amino-acid frequencies and other parameters in the genetic code's evolution, in an attempt to propose a tentative picture of primitive life.

The origin of the genetic code cannot be studied using measurements based on the PAM matrix because this matrix reflects the code itself, making any such analyses tautologous

Freeland et al. (Mol. Biol. Evol. 2000 a, 17, 511--518) have recently used a transformation of the PAM 74-100 matrix to study the level of optimization reached during genetic code origin. Since the PAM matrix counts the amino acid substitutions that occurred in families of homologous proteins during molecular evolution and as this process is mediated by the genetic code structure itself, it could be that the influence of the code on this matrix is such as to make any conclusion insignificant. As will be shown in the present paper, the transformation of the PAM matrix is affected in a non-marginal way by the organization of the genetic code and, thus, renders the analysis of Freeland et al. tautologous. Although, under the hypothesis of a highly optimized genetic code, some correlations may be expected between a measurement of similarity between amino acids and the genetic code structure, no certain conclusions can be drawn for the measurement used by Freeland et al.