The Influence of the Selection at the Amino Acid Level on Synonymous Codon Usage from the Viewpoint of Alternative Genetic Codes.

Synonymous codon usage can be influenced by mutations and/or selection, e.g., for speed of protein translation and correct folding. However, this codon bias can also be affected by a general selection at the amino acid level due to differences in the acceptance of the loss and generation of these codons. To assess the importance of this effect, we constructed a mutation-selection model model, in which we generated almost 90,000 stationary nucleotide distributions produced by mutational processes and applied a selection based on differences in physicochemical properties of amino acids. Under these conditions, we calculated the usage of fourfold degenerated (4FD) codons and compared it with the usage characteristic of the pure mutations. We considered both the standard genetic code (SGC) and alternative genetic codes (AGCs). The analyses showed that a majority of AGCs produced a greater 4FD codon bias than the SGC. The mutations producing more thymine or adenine than guanine and cytosine increased the differences in usage. On the other hand, the mutational pressures generating a lot of cytosine or guanine with a low content of adenine and thymine decreased this bias because the nucleotide content of most 4FD codons stayed in the compositional equilibrium with these pressures. The comparison of the theoretical results with those for real protein coding sequences showed that the influence of selection at the amino acid level on the synonymous codon usage cannot be neglected. The analyses indicate that the effect of amino acid selection cannot be disregarded and that it can interfere with other selection factors influencing codon usage, especially in AT-rich genomes, in which AGCs are usually used.

The structure of the genetic code as an optimal graph clustering problem.

The standard genetic code (SGC) is the set of rules by which genetic information is translated into proteins, from codons, i.e. triplets of nucleotides, to amino acids. The questions about the origin and the main factor responsible for the present structure of the code are still under a hot debate. Various methodologies have been used to study the features of the code and assess the level of its potential optimality. Here, we introduced a new general approach to evaluate the quality of the genetic code structure. This methodology comes from graph theory and allows us to describe new properties of the genetic code in terms of conductance. This parameter measures the robustness of codon groups against the potential changes in translation of the protein-coding sequences generated by single nucleotide substitutions. We described the genetic code as a partition of an undirected and unweighted graph, which makes the model general and universal. Using this approach, we showed that the structure of the genetic code is a solution to the graph clustering problem. We presented and discussed the structure of the codes that are optimal according to the conductance. Despite the fact that the standard genetic code is far from being optimal according to the conductance, its structure is characterised by many codon groups reaching the minimum conductance for their size. The SGC represents most likely a local minimum in terms of errors occurring in protein-coding sequences and their translation.

Model of Genetic Code Structure Evolution under Various Types of Codon Reading.

The standard genetic code (SGC) is a set of rules according to which 64 codons are assigned to 20 canonical amino acids and stop coding signal. As a consequence, the SGC is redundant because there is a greater number of codons than the number of encoded labels. This redundancy implies the existence of codons that encode the same genetic information. The size and organization of such synonymous codon blocks are important characteristics of the SGC structure whose evolution is still unclear. Therefore, we studied possible evolutionary mechanisms of the codon block structure. We conducted computer simulations assuming that coding systems at early stages of the SGC evolution were sets of ambiguous codon assignments with high entropy. We included three types of reading systems characterized by different inaccuracy and pattern of codon recognition. In contrast to the previous study, we allowed for evolution of the reading systems and their competition. The simulations performed under minimization of translational errors and reduction of coding ambiguity produced the coding system resistant to these errors. The reading system similar to that present in the SGC dominated the others very quickly. The survived system was also characterized by low entropy and possessed properties similar to that in the SGC. Our simulation show that the unambiguous SGC could emerged from a code with a lower level of ambiguity and the number of tRNAs increased during the evolution.

Models of genetic code structure evolution with variable number of coded labels.

It is assumed that at the early stage of cell evolution its translation machinery was characterized by high noise, i.e. ambiguous assignment of codons to amino acids in the genetic code, which initially encoded only few amino acids. Next, during its evolution new amino acids were added to this code. Taking into account this facts, we investigated theoretical models of genetic code's structure, which evolved from a set of ambiguous codons assignments into a coding system with a low level of uncertainty. We considered three types of translational inaccuracies assuming a different number of fixed codon positions. We applied a modified version of evolutionary algorithm for finding the genetic codes that the most effectively reduced the initial uncertainty in the assignment of codons to encoded labels, i.e. amino acids and a stop translation signal. We examined codes with the number of labels from four to 22. Our results indicated that the quality of genetic code structure is strongly dependent on the number of encoded labels as well as the type of translational mechanism. The more strict assignments of codon to the labels was preferred by the codes encoding more number of labels. The results showed that a smaller degeneracy of codes evolved from a more tolerant coding with the stepwise addition of coded amino acids to the genetic code. The distribution of codon groups in the standard genetic code corresponds well to the translation model assuming two fixed codon positions, whereas the six-codon groups can be relics form previous stages of evolution when the code characterized by a greater uncertainty.

Some theoretical aspects of reprogramming the standard genetic code.

Reprogramming of the standard genetic code to include non-canonical amino acids (ncAAs) opens new prospects for medicine, industry, and biotechnology. There are several methods of code engineering, which allow us for storing new genetic information in DNA sequences and producing proteins with new properties. Here, we provided a theoretical background for the optimal genetic code expansion, which may find application in the experimental design of the genetic code. We assumed that the expanded genetic code includes both canonical and non-canonical information stored in 64 classical codons. What is more, the new coding system is robust to point mutations and minimizes the possibility of reversion from the new to old information. In order to find such codes, we applied graph theory to analyze the properties of optimal codon sets. We presented the formal procedure in finding the optimal codes with various number of vacant codons that could be assigned to new amino acids. Finally, we discussed the optimal number of the newly incorporated ncAAs and also the optimal size of codon groups that can be assigned to ncAAs.

Basic principles of the genetic code extension.

Compounds including non-canonical amino acids (ncAAs) or other artificially designed molecules can find a lot of applications in medicine, industry and biotechnology. They can be produced thanks to the modification or extension of the standard genetic code (SGC). Such peptides or proteins including the ncAAs can be constantly delivered in a stable way by organisms with the customized genetic code. Among several methods of engineering the code, using non-canonical base pairs is especially promising, because it enables generating many new codons, which can be used to encode any new amino acid. Since even one pair of new bases can extend the SGC up to 216 codons generated by a six-letter nucleotide alphabet, the extension of the SGC can be achieved in many ways. Here, we proposed a stepwise procedure of the SGC extension with one pair of non-canonical bases to minimize the consequences of point mutations. We reported relationships between codons in the framework of graph theory. All 216 codons were represented as nodes of the graph, whereas its edges were induced by all possible single nucleotide mutations occurring between codons. Therefore, every set of canonical and newly added codons induces a specific subgraph. We characterized the properties of the induced subgraphs generated by selected sets of codons. Thanks to that, we were able to describe a procedure for incremental addition of the set of meaningful codons up to the full coding system consisting of three pairs of bases. The procedure of gradual extension of the SGC makes the whole system robust to changing genetic information due to mutations and is compatible with the views assuming that codons and amino acids were added successively to the primordial SGC, which evolved minimizing harmful consequences of mutations or mistranslations of encoded proteins.

Resolving Phylogenetic Relationships within Passeriformes Based on Mitochondrial Genes and Inferring the Evolution of Their Mitogenomes in Terms of Duplications.

Mitochondrial genes are placed on one molecule, which implies that they should carry consistent phylogenetic information. Following this advantage, we present a well-supported phylogeny based on mitochondrial genomes from almost 300 representatives of Passeriformes, the most numerous and differentiated Aves order. The analyses resolved the phylogenetic position of paraphyletic Basal and Transitional Oscines. Passerida occurred divided into two groups, one containing Paroidea and Sylvioidea, whereas the other, Passeroidea and Muscicapoidea. Analyses of mitogenomes showed four types of rearrangements including a duplicated control region (CR) with adjacent genes. Mapping the presence and absence of duplications onto the phylogenetic tree revealed that the duplication was the ancestral state for passerines and was maintained in early diverged lineages. Next, the duplication could be lost and occurred independently at least four times according to the most parsimonious scenario. In some lineages, two CR copies have been inherited from an ancient duplication and highly diverged, whereas in others, the second copy became similar to the first one due to concerted evolution. The second CR copies accumulated over twice as many substitutions as the first ones. However, the second CRs were not completely eliminated and were retained for a long time, which suggests that both regions can fulfill an important role in mitogenomes. Phylogenetic analyses based on CR sequences subjected to the complex evolution can produce tree topologies inconsistent with real evolutionary relationships between species. Passerines with two CRs showed a higher metabolic rate in relation to their body mass.

The influence of different types of translational inaccuracies on the genetic code structure.

BACKGROUND: The standard genetic code is a recipe for assigning unambiguously 21 labels, i.e. amino acids and stop translation signal, to 64 codons. However, at early stages of the translational machinery development, the codons did not have to be read unambiguously and the early genetic codes could have contained some ambiguous assignments of codons to amino acids. Therefore, the goal of this work was to obtain the genetic code structures which could have evolved assuming different types of inaccuracy of the translational machinery starting from unambiguous assignments of codons to amino acids. RESULTS: We developed a theoretical model assuming that the level of uncertainty of codon assignments can gradually decrease during the simulations. Since it is postulated that the standard code has evolved to be robust against point mutations and mistranslations, we developed three simulation scenarios assuming that such errors can influence one, two or three codon positions. The simulated codes were selected using the evolutionary algorithm methodology to decrease coding ambiguity and increase their robustness against mistranslation. CONCLUSIONS: The results indicate that the typical codon block structure of the genetic code could have evolved to decrease the ambiguity of amino acid to codon assignments and to increase the fidelity of reading the genetic information. However, the robustness to errors was not the decisive factor that influenced the genetic code evolution because it is possible to find theoretical codes that minimize the reading errors better than the standard genetic code.

Many alternative and theoretical genetic codes are more robust to amino acid replacements than the standard genetic code.

We evaluated the differences between the standard genetic code (SGC) and its known alternative variants in terms of the consequences of amino acids replacements. Furthermore, the properties of all the possible theoretical genetic codes, which differ from the SGC by one, two or three changes in codon assignments were also tested. Although the SGC is closer to the best theoretical codes than to the worst ones due to the minimization of amino acid replacements, from 10% to 27% of the all possible theoretical codes minimize the effect of these replacements better than the SGC. Interestingly, many types of codon reassignments observed in the alternative codes are also responsible for the substantial robustness to amino acid replacements. As many as 18 out of 21 alternatives perform better than the SGC under the assumed optimization criteria. These findings suggest that not all reassignments in the alternative codes are neutral and some of them could be selected to reduce harmful effects of mutations or translation of protein-coding sequences. The results also imply that the standard genetic code can be improved in this respect by a quite small number of changes, which are in fact realized in its variants. It would mean that the tendency to minimize mutational errors was not the main force that drove the evolution of the SGC.

The optimality of the standard genetic code assessed by an eight-objective evolutionary algorithm.

BACKGROUND: The standard genetic code (SGC) is a unique set of rules which assign amino acids to codons. Similar amino acids tend to have similar codons indicating that the code evolved to minimize the costs of amino acid replacements in proteins, caused by mutations or translational errors. However, if such optimization in fact occurred, many different properties of amino acids must have been taken into account during the code evolution. Therefore, this problem can be reformulated as a multi-objective optimization task, in which the selection constraints are represented by measures based on various amino acid properties. RESULTS: To study the optimality of the SGC we applied a multi-objective evolutionary algorithm and we used the representatives of eight clusters, which grouped over 500 indices describing various physicochemical properties of amino acids. Thanks to that we avoided an arbitrary choice of amino acid features as optimization criteria. As a consequence, we were able to conduct a more general study on the properties of the SGC than the ones presented so far in other papers on this topic. We considered two models of the genetic code, one preserving the characteristic codon blocks structure of the SGC and the other without this restriction. The results revealed that the SGC could be significantly improved in terms of error minimization, hereby it is not fully optimized. Its structure differs significantly from the structure of the codes optimized to minimize the costs of amino acid replacements. On the other hand, using newly defined quality measures that placed the SGC in the global space of theoretical genetic codes, we showed that the SGC is definitely closer to the codes that minimize the costs of amino acids replacements than those maximizing them. CONCLUSIONS: The standard genetic code represents most likely only partially optimized systems, which emerged under the influence of many different factors. Our findings can be useful to researchers involved in modifying the genetic code of the living organisms and designing artificial ones.

Correction: Optimization of the standard genetic code according to three codon positions using an evolutionary algorithm.

[This corrects the article DOI: 10.1371/journal.pone.0201715.].

Optimization of the standard genetic code according to three codon positions using an evolutionary algorithm.

Many biological systems are typically examined from the point of view of adaptation to certain conditions or requirements. One such system is the standard genetic code (SGC), which generally minimizes the cost of amino acid replacements resulting from mutations or mistranslations. However, no full consensus has been reached on the factors that caused the evolution of this feature. One of the hypotheses suggests that code optimality was directly selected as an advantage to preserve information about encoded proteins. An important feature that should be considered when studying the SGC is the different roles of the three codon positions. Therefore, we investigated the robustness of this code regarding the cost of amino acid replacements resulting from substitutions in these positions separately and the sum of these costs. We applied a modified evolutionary algorithm and included four models of the genetic code assuming various restrictions on its structure. The SGC was compared both with the codes that minimize the objective function and those that maximize it. This approach allowed us to place the SGC in the global space of possible codes, which is a more appropriate and unbiased comparison than that with randomly generated codes because they are characterized by relatively uniform amino acid assignments to codons. The SGC appeared to be well optimized at the global scale, but its individual positions were not fully optimized because there were codes that were optimized for only one codon position and simultaneously outperformed the SGC at the other positions. We also found that different code structures may lead to the same optimality and that random codes can show a tendency to minimize costs under some of the genetic code models. Our results suggest that the optimality of SGC could be a by-product of other processes.

Role of recombination and faithfulness to partner in sex chromosome degeneration.

Sex determination in mammals is strongly linked to sex chromosomes. In most cases, females possess two copies of X chromosome while males have one X and one Y chromosome. It is assumed that these chromosomes originated from a pair of homologous autosomes, which diverged when recombination between them was suppressed. However, it is still debated why the sex chromosomes stopped recombining and how this process spread out over most part of the chromosomes. To study this problem, we developed a simulation model, in which the recombination rate between the sex chromosomes can freely evolve. We found that the suppression of recombination between the X and Y is spontaneous and proceeds very quickly during the evolution of population, which leads to the degeneration of the Y in males. Interestingly, the degeneration happens only when mating pairs are unfaithful. This evolutionary strategy purifies the X chromosome from defective alleles and leads to the larger number of females than males in the population. In consequence, the reproductive potential of the whole population increases. Our results imply that both the suppression of recombination and the degeneration of Y chromosome may be associated with reproductive strategy and favoured in polygamous populations with faithless mating partners.

Evolutionary history and phylogeographic relationships of shrews from Sorex araneus group.

Shrews of the Sorex genus are an evolutionarily successful group that includes more than 77 species widely distributed in Eurasia and North America. The genus is one of the rare cases where karyotypic changes reflect well the evolutionary relationships among its species. The taxa showing the greatest variation in karyotype are usually classified into the Sorex araneus group. Its evolution was associated with chromosomal rearrangements, which could have promoted fast diversification of this group into many chromosomal races and species. These processes were additionally complicated by introgressions of mitochondrial DNA, which made the evolutionary history of this group quite complex and difficult to infer. To tackle the problem, we performed multi-method phylogenetic analyses based on mitochondrial cytochrome b that is considered a good molecular marker available for many representatives of Sorex. The results were compared with phylogenies based on chromosomal rearrangement data and put into temporal and spatial context using molecular dating and historical biogeography methods. We complemented the study with the estimation of diversification rates within the S. araneus group as well as comparing the results with paleontological records and climatic oscillations within the last 4 million years. Based on the gathered data, we proposed a hypothetical scenario for the evolution and geographic dispersion of species belonging to the S. araneus group. The shrews began to diversify about 2.7 million years ago in Eurasia and then migrated at least twice to North America. The evolution of shrews was driven by Pleistocene glacial and interglacial cycles, which increased their speciation rate and the emergence of new lineages. The migrations of populations were accompanied by introgressions of mitochondrial DNA into native shrews and occurred at least twice.

Optimization of amino acid replacement costs by mutational pressure in bacterial genomes.

Mutations are considered a spontaneous and random process, which is important component of evolution because it generates genetic variation. On the other hand, mutations are deleterious leading to non-functional genes and energetically costly repairs. Therefore, one can expect that the mutational pressure is optimized to simultaneously generate genetic diversity and preserve genetic information. To check if empirical mutational pressures are optimized in these ways, we compared matrices of nucleotide mutation rates derived from bacterial genomes with their best possible alternatives that minimized or maximized costs of amino acid replacements associated with differences in their physicochemical properties (e.g. hydropathy and polarity). It should be noted that the studied empirical nucleotide substitution matrices and the costs of amino acid replacements are independent because these matrices were derived from sites free of selection on amino acid properties and the amino acid costs assumed only amino acid physicochemical properties without any information about mutation at the nucleotide level. Obtained results indicate that the empirical mutational matrices show a tendency to minimize costs of amino acid replacements. It implies that bacterial mutational pressures can evolve to decrease consequences of amino acid substitutions. However, the optimization is not full, which enables generation of some genetic variability.

The Impact of Selection at the Amino Acid Level on the Usage of Synonymous Codons.

There are two main forces that affect usage of synonymous codons: directional mutational pressure and selection. The effectiveness of protein translation is usually considered as the main selectional factor. However, biased codon usage can also be a byproduct of a general selection at the amino acid level interacting with nucleotide replacements. To evaluate the validity and strength of such an effect, we superimposed >3.5 billion unrestricted mutational processes on the selection of nonsynonymous substitutions based on the differences in physicochemical properties of the coded amino acids. Using a modified evolutionary optimization algorithm, we determined the conditions in which the effect on the relative codon usage is maximized. We found that the effect is enhanced by mutational processes generating more adenine and thymine than guanine and cytosine, as well as more purines than pyrimidines. Interestingly, this effect is observed only under an unrestricted model of nucleotide substitution, and disappears when the mutational process is time-reversible. Comparison of the simulation results with data for real protein coding sequences indicates that the impact of selection at the amino acid level on synonymous codon usage cannot be neglected. Furthermore, it can considerably interfere, especially in AT-rich genomes, with other selections on codon usage, e.g., translational efficiency. It may also lead to difficulties in the recognition of other effects influencing codon bias, and an overestimation of protein coding sequences whose codon usage is subjected to adaptational selection.

Peculiarities within peculiarities - dinoflagellates and their mitochondrial genomes.

After the establishment of an endosymbiotic relationship between a proto-mitochondrion and its probable archaeal host, mitochondrial genomes underwent a spectacular reductive evolution. An interesting pathway was chosen by mitogenomes of unicellular protists called dinoflagellates, which experienced an additional wave of reduction followed by amplification and rearrangement leading to their secondary complexity. The former resulted in a mitogenome consisting of only three protein-coding genes, the latter in their multiple copies being scattered across numerous chromosomes and the evolution of complex processes for their expression. These stunning features raise a question about the future of the dinoflagellate mitochondrial genome.

Distribution of recombination hotspots in the human genome--a comparison of computer simulations with real data.

Recombination is the main cause of genetic diversity. Thus, errors in this process can lead to chromosomal abnormalities. Recombination events are confined to narrow chromosome regions called hotspots in which characteristic DNA motifs are found. Genomic analyses have shown that both recombination hotspots and DNA motifs are distributed unevenly along human chromosomes and are much more frequent in the subtelomeric regions of chromosomes than in their central parts. Clusters of motifs roughly follow the distribution of recombination hotspots whereas single motifs show a negative correlation with the hotspot distribution. To model the phenomena related to recombination, we carried out computer Monte Carlo simulations of genome evolution. Computer simulations generated uneven distribution of hotspots with their domination in the subtelomeric regions of chromosomes. They also revealed that purifying selection eliminating defective alleles is strong enough to cause such hotspot distribution. After sufficiently long time of simulations, the structure of chromosomes reached a dynamic equilibrium, in which number and global distribution of both hotspots and defective alleles remained statistically unchanged, while their precise positions were shifted. This resembles the dynamic structure of human and chimpanzee genomes, where hotspots change their exact locations but the global distributions of recombination events are very similar.

Genome analyses and modelling the relationships between coding density, recombination rate and chromosome length.

In the human genomes, recombination frequency between homologous chromosomes during meiosis is highly correlated with their physical length while it differs significantly when their coding density is considered. Furthermore, it has been observed that the recombination events are distributed unevenly along the chromosomes. We have found that many of such recombination properties can be predicted by computer simulations of population evolution based on the Monte Carlo methods. For example, these simulations have shown that the probability of acceptance of the recombination events by selection is higher at the ends of chromosomes and lower in their middle parts. The regions of high coding density are more prone to enter the strategy of haplotype complementation and to form clusters of genes, which are "recombination deserts". The phenomenon of switching in-between the purifying selection and haplotype complementation has a phase transition character, and many relations between the effective population size, coding density, chromosome size and recombination frequency are those of the power law type.

Sympatric speciation as intrinsic property of the expanding population.

Sympatric speciation is still debatable, though some well documented empirical data that support it already exist. Our computer modeling reveals that sympatric speciation is an intrinsic property of the expanding populations with differentiated inbreeding-higher at the edges and lower inside the territory. At the edges of expanding populations, the probability of forming deleterious phenotypes by placing two defective alleles in the corresponding loci is relatively high even with low genetic load. Thus, the winning strategy is to use rather the complementary haplotypes to form zygotes. This strategy leads to a very fast sympatric speciation and specific distribution of recombination activity along the chromosomes-higher at the subtelomeric regions (close to the ends of chromosomes) and lower in the middle of chromosomes, which is also observed in all human chromosomes (excluding Y).