The complementary sex determiner (csd) gene determines the sex of the western honey bee (Apis mellifera L.). Bees that are heterozygous at the csd locus develop into females; whereas hemizygous bees develop into males. The co-occurrence of two identical csd alleles in a single diploid genome leads to the genetic death of the bee. Thus, the maintenance of csd diversity in the population is favoured. The number and distribution of csd alleles is particularly interesting in light of the recent decline in the honey bee population. In this study, we analysed the distribution of csd alleles in two Polish populations separated by about 100 km. We analysed the maternal alleles of 193 colonies and found 121 different alleles. We also analysed the distribution and frequency of the alleles, and found that they are distributed unevenly. We show that the methods that have been used so far to estimate the total worldwide number of csd alleles have significantly underestimated their diversity. We also show that the uneven distribution of csd alleles is caused by a large number of infrequent alleles, which most likely results from the fact that these alleles are generated very frequently.

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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.

BackgroundThe 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.ResultsTo 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.ConclusionsThe 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.

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.

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The standard genetic code (SGC) is the set of rules by which genetic information is 1 translated into proteins, from codons, i.e. triplets of nucleotides, to amino acids. The 2 questions about the origin and the main factor responsible for the present structure of sequences generated by single nucleotide substitutions. We described the genetic code as 10 a partition of an undirected and unweighted graph, which makes the model general and 11 universal. Using this approach, we showed that the structure of the genetic code is a 12 solution to the graph clustering problem. We presented and discussed the structure of 13 the codes that are optimal according to the conductance. Despite the fact that the 14 standard genetic code is far from being optimal according to the conductance, its 15 structure is characterised by many codon groups reaching the minimum conductance for 16 their size. The SGC represents most likely a local minimum in terms of errors occurring 17 in protein-coding sequences and their translation.

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