Variation in the pattern of synonymous and nonsynonymous difference between two fungal genomes

The proportion of synonymous nucleotide differences per synonymous site (p(S)) and the proportion of nonsynonymous differences per nonsynonymous site (p(N)) were computed at 1,993,217 individual codons in 4,133 protein-coding genes between the two yeast species Saccharomyces cerevisiae and Saccharomyces paradoxus. When the modified Nei-Gojobori method was used, significantly more codons with p(N) > p(S) were observed than expected, based on random pairing of observed p(S) and p(N) values. However, this finding was most likely explained by the presence of a strong negative correlation between the number of synonymous differences and the number of nonsynonymous differences at codons with at least one difference. As a result of this correlation, codons with p(N) > p(S) were characterized not only by unusually high p(N) but also by unusually low p(S). On the other hand, the number of codons with p(N)>p(S) (where p(S) is the mean p(S) for all codons) was very similar to the random expectation, and the observed number of 30-codon windows with p(N) > p(S) was significantly lower than the random expectation. These results imply that the occurrence of a certain number of codons or codon windows with p(N) > p(S) is expected given the nature of nucleotide substitution and need not imply the action of positive Darwinian selection.     

Widespread positive selection in synonymous sites of mammalian genes

Evolution of protein sequences is largely governed by purifying selection, with a small fraction of proteins evolving under positive selection. The evolution at synonymous positions in protein-coding genes is not nearly as well understood, with the extent and types of selection remaining, largely, unclear. A statistical test to identify purifying and positive selection at synonymous sites in protein-coding genes was developed. The method compares the rate of evolution at synonymous sites (Ks) to that in intron sequences of the same gene after sampling the aligned intron sequences to mimic the statistical properties of coding sequences. We detected purifying selection at synonymous sites in approximately 28% of the 1,562 analyzed orthologous genes from mouse and rat, and positive selection in approximately 12% of the genes. Thus, the fraction of genes with readily detectable positive selection at synonymous sites is much greater than the fraction of genes with comparable positive selection at nonsynonymous sites, i.e., at the level of the protein sequence. Unlike other genes, the genes with positive selection at synonymous sites showed no correlation between Ks and the rate of evolution in nonsynonymous sites (Ka), indicating that evolution of synonymous sites under positive selection is decoupled from protein evolution. The genes with purifying selection at synonymous sites showed significant anticorrelation between Ks and expression level and breadth, indicating that highly expressed genes evolve slowly. The genes with positive selection at synonymous sites showed the opposite trend, i.e., highly expressed genes had, on average, higher Ks. For the genes with positive selection at synonymous sites, a significantly lower mRNA stability is predicted compared to the genes with negative selection. Thus, mRNA destabilization could be an important factor driving positive selection in nonsynonymous sites, probably, through regulation of expression at the level of mRNA degradation and, possibly, also translation rate. So, unexpectedly, we found that positive selection at synonymous sites of mammalian genes is substantially more common than positive selection at the level of protein sequences. Positive selection at synonymous sites might act through mRNA destabilization affecting mRNA levels and translation.     

The comparative method rules! Codon volatility cannot detect positive Darwinian selection using a single genome sequence

All established methods for detecting positive selection at the molecular level rely on comparisons between nucleotide sequences. An exceptional method that purports to detect selection on the basis of a single genomic sequence has recently been proposed. This method uses a measure called "codon volatility," defined for each codon as the ratio between the number of nonsynonymous codons that differ from the codon under study at a single nucleotide position and the number of sense codons that differ from the codon under study at a single nucleotide position. Here, we examine various properties of codon volatility and its derivatives and use simulation of evolutionary processes to determine whether they can be used to detect selective pressures. Codons for only four amino acids (glycine, leucine, arginine, and serine) show any variation in codon volatility. Thus, codon volatility is mainly a proxy for amino acid usage, rather than for codon usage, with 65% of all synonymous changes and 27% of all nonsynonymous changes being undetectable by this measure. Genes identified by the volatility method as being subject to positive selection tend to have idiosyncratic amino acid compositions (e.g., they are glycine rich or arginine poor). An additional property of codon volatility is the near zero variance of its mean expectation, which translates into overestimated statistical significance estimates, especially in the absence of corrections for multiple comparisons. A comparison with measures of selection inferred through comparative methodology reveals no relationship between the results of the two methods. Finally, we show that codon volatility can increase in the absence of positive Darwinian selection; that is, increased codon volatility is not indicative of positive selection.     

The effect of positive selection on a sexual reproduction gene in Thalassiosira weissflogii (Bacillariophyta): results obtained from maximum-likelihood and parsimony-based methods

Maximum-Likelihood-based and parsimony-based methods were used to test for potential effects of positive selection on the sexually induced gene 1 (Sig1) in Thalassiosira weissflogii. The Sig proteins are thought to play a role in mediating sperm-egg recognition during the sexual reproduction phase. The results obtained from parsimony-based analyses showed that none of the amino acid sites were influenced by positive selection. Maximum-likelihood analyses indicated that positive selection was affecting a maximum of seven and a minimum of four amino acid sites in the polypeptide derived from Sig1. It was concluded that the results obtained from the maximum-likelihood-based method are more reliable than those obtained from the parsimony-based approach. This is apparently the first study that has shown that reproductive proteins in unicellular eukaryotes are influenced by positive selection.     

The pattern of nucleotide difference at individual codons among mouse, rat, and human

The patterns of nucleotide difference were compared at 3,473,111 codons from 9,390 aligned orthologous genes of mouse (Mus musculus), rat (Rattus norvegicus), and human (Homo sapiens). The results showed evidence of a higher frequency of both synonymous and nonsynonymous differences from human in the rat than in the mouse. However, contrary to a previous report, there was no evidence of a greater frequency of codons with multiple nonsynonymous substitutions between the two rodent species than expected under random substitution.     

Codon volatility as an indicator of positive selection: data from eukaryotic genome comparisons

It has been suggested that codon volatility (the proportion of the point-mutation neighbors of a codon that encode different amino acids) can be used as an index of past positive selection. We compared codon volatility with patterns of synonymous and nonsynonymous nucleotide substitution in genome-wide comparisons of orthologous genes between three pairs of related genomes: (1) the protists Plasmodium falciparum and P. yoelii, (2) the fungi Saccharomyces cerevisiae and S. paradoxus, and (3) the mammals mouse and rat. Codon volatility was not consistently associated with an elevated rate of nonsynonymous substitution, as would be expected under positive selection. Rather, the most consistent and powerful correlate of elevated codon volatility was nucleotide content at the second codon position, as expected, given the nature of the genetic code.     

Statistical analysis of the envelope gene and the prM region of Japanese encephalitis virus: Evidence suggestive of positive selection

The variation in nucleotide sequence observed in the envelope (E) gene and the prM (precursor of M protein) region of different strains of Japanese encephalitis virus (JEV) was analysed. Presence of selective forces acting on these regions was investigated by computing the relative rates of synonymous (K _s) and nonsynonymous (K _a) substitutions. The ratioK _s/K _a was used as an indicator of the overall selective constraints on the amino acid sequence of JEV proteins. The possibility that different regions of the gene may be subject to varying selective pressures was tested by dividing the gene into three regions and estimating theK _s/K _a ratio for each region. On the basis of analysis of a limited number (17) of strains of JEV, evidence suggestive of positive selection acting on certain regions of the E gene of the virus, and in some cases on the entire gene, was obtained. Analysis ofK _a diversity in the prM region of 46 JEV strains grouped into three genotypes revealed that strains included in genotype II were more heterogeneous than strains belonging to genotype I, while the differences between meanK _a values for genotypes I and III and genotypes II and III were not statistically significant. Analysis of host-specific heterogeneity in the prM region revealed that pig isolates were more X_a-diverse than human isolates.     

Evaluation of six methods for estimating synonymous and nonsynonymous substitution rates

Methods for estimating synonymous and nonsynonymous substitution rates among protein-coding sequences adopt different mutation （substitution） models with subtle yet significant differences, which lead to different estimates of evolutionary information. Little attention has been devoted to the comparison of methods for obtaining reliable estimates since the amount of sequence variations within targeted datasets is always unpredictable. To our knowledge, there is little information available in literature about evaluation of these different methods. In this study, we compared six widely used methods and provided with evaluation results using simulated sequences. The results indicate that incorporating sequence features （such as transition/transversion bias and nucleotide/codon frequency bias） into methods could yield better performance. We recommend that conclusions related to or derived from Ka and Ks analyses should not be readily drawn only according to results from one method.     

Site-to-site variation of synonymous substitution rates

We develop a new model for studying the molecular evolution of protein-coding DNA sequences. In contrast to existing models, we incorporate the potential for site-to-site heterogeneity of both synonymous and nonsynonymous substitution rates. We demonstrate that within-gene heterogeneity of synonymous substitution rates appears to be common. Using the new family of models, we investigate the utility of a variety of new statistical inference procedures, and we pay particular attention to issues surrounding the detection of sites undergoing positive selection. We discuss how failure to model synonymous rate variation in the model can lead to misidentification of sites as positively selected.     

Comparison of three methods for estimating rates of synonymous and nonsynonymous nucleotide substitutions

Three frequently used methods for estimating the synonymous and nonsynonymous substitution rates (Ks and Ka) were evaluated and compared for their accuracies; these methods are denoted by LWL85, LPB93, and GY94, respectively. For this purpose, we used a codon-evolution model to obtain the expected Ka and Ks values for the above three methods and compared the values with those obtained by the three methods. We also proposed some modifications of LWL85 and LPB93 to increase their accuracies. Our computer simulations under the codon-evolution model showed that for sequences < or =300 codons, the performance of GY94 may not be reliable. For longer sequences, GY94 is more accurate for estimating the Ka/Ks ratio than the modified LPB93 and LWL85 in the majority of the cases studied. This is particularly so when k > or = 3, which is the transition/transversion (mutation) rate ratio. However, when k is approximately 2 and when the sequence divergence is relatively large, the modified LWL85 performed better than GY94 and the modified LPB93. The inferiority of LPB93 to LWL85 is surprising because LPB93 was intended to improve LWL85. Also, it has been thought that the codon-based method of GY94 is better than the heuristic method of LWL85, but our simulation results showed that in many cases, the opposite was true, even though our simulation was based on the codon-evolution model.     

Why Do More Divergent Sequences Produce Smaller Nonsynonymous/Synonymous Rate Ratios in Pairwise Sequence Comparisons?

Several studies have reported a negative correlation between estimates of the nonsynonymous to synonymous rate ratio (ω = d_N/d_S) and the sequence distance d in pairwise comparisons of the same gene from different species. That is, more divergent sequences produce smaller estimates of ω. Explanations for this negative correlation have included segregating nonsynonymous polymorphisms in closely related species and nonlinear dynamics of the ratio of two random variables. Here we study the statistical properties of the maximum-likelihood estimates of ω and d in pairwise alignments and explore the possibility that the negative correlation can be entirely explained by those properties. We show that the ω estimate is positively biased for small d and that the bias decreases with the increase of d. We also show that the estimates of ω and d are negatively correlated when ω < 1 and positively correlated when ω > 1. However, the bias in estimates of ω and the correlation between estimates of ω and d are not enough to explain the much stronger correlation observed in real data sets. We then explore the behavior of the estimates when the model is misspecified and suggest that the observed correlation may be due to protein-level selection that causes very different amino acids to be favored in different domains of the protein. Widely used models fail to account for such among-site heterogeneity and cause underestimation of the nonsynonymous rate and ω, with the bias being much stronger for distant sequences. We point out that tests of positive selection based on the ω ratio are invariant to the parameterization of the model and thus unaffected by bias in the ω estimates or the correlation between estimates of ω and d.