Effects of gene expression on molecular evolution in Arabidopsis thaliana and Arabidopsis lyrata

We analyzed the complete genome sequence of Arabidopsis thaliana and sequence data from 83 genes in the outcrossing A. lyrata, to better understand the role of gene expression on the strength of natural selection on synonymous and replacement sites in Arabidopsis. From data on tRNA gene abundance, we find a good concordance between codon preferences and the relative abundance of isoaccepting tRNAs in the complete A. thaliana genome, consistent with models of translational selection. Both EST-based and new quantitative measures of gene expression (MPSS) suggest that codon preferences derived from information on tRNA abundance are more strongly associated with gene expression than those obtained from multivariate analysis, which provides further support for the hypothesis that codon bias in Arabidopsis is under selection mediated by tRNA abundance. Consistent with previous results, analysis of protein evolution reveals a significant correlation between gene expression level and amino acid substitution rate. Analysis by MPSS estimates of gene expression suggests that this effect is primarily the result of a correlation between the number of tissues in which a gene is expressed and the rate of amino acid substitution, which indicates that the degree of tissue specialization may be an important determinant of the rate of protein evolution in Arabidopsis.     

Benchmarking Multi-Rate Codon Models

The single rate codon model of non-synonymous substitution is ubiquitous in phylogenetic modeling. Indeed, the use of a non-synonymous to synonymous substitution rate ratio parameter has facilitated the interpretation of selection pressure on genomes. Although the single rate model has achieved wide acceptance, we argue that the assumption of a single rate of non-synonymous substitution is biologically unreasonable, given observed differences in substitution rates evident from empirical amino acid models. Some have attempted to incorporate amino acid substitution biases into models of codon evolution and have shown improved model performance versus the single rate model. Here, we show that the single rate model of non-synonymous substitution is easily outperformed by a model with multiple non-synonymous rate classes, yet in which amino acid substitution pairs are assigned randomly to these classes. We argue that, since the single rate model is so easy to improve upon, new codon models should not be validated entirely on the basis of improved model fit over this model. Rather, we should strive to both improve on the single rate model and to approximate the general time-reversible model of codon substitution, with as few parameters as possible, so as to reduce model over-fitting. We hint at how this can be achieved with a Genetic Algorithm approach in which rate classes are assigned on the basis of sequence information content.     

An empirical examination of the utility of codon-substitution models in phylogeny reconstruction

Models of codon substitution have been commonly used to compare protein-coding DNA sequences and are particularly effective in detecting signals of natural selection acting on the protein. Their utility in reconstructing molecular phylogenies and in dating species divergences has not been explored. Codon models naturally accommodate synonymous and nonsynonymous substitutions, which occur at very different rates and may be informative for recent and ancient divergences, respectively. Thus codon models may be expected to make an efficient use of phylogenetic information in protein-coding DNA sequences. Here we applied codon models to 106 protein-coding genes from eight yeast species to reconstruct phylogenies using the maximum likelihood method, in comparison with nucleotide- and amino acid-based analyses. The results appeared to confirm that expectation. Nucleotide-based analysis, under simplistic substitution models, were efficient in recovering recent divergences whereas amino acid-based analysis performed better at recovering deep divergences. Codon models appeared to combine the advantages of amino acid and nucleotide data and had good performance at recovering both recent and deep divergences. Estimation of relative species divergence times using amino acid and codon models suggested that translation of gene sequences into proteins led to information loss of from 30% for deep nodes to 66% for recent nodes. Although computational burden makes codon models unfeasible for tree search in large data sets, we suggest that they may be useful for comparing candidate trees. Nucleotide models that accommodate the differences in evolutionary dynamics at the three codon positions also performed well, at much less computational cost. We discuss the relationship between a model's fit to data and its utility in phylogeny reconstruction and caution against use of overly complex substitution models.     

Models of amino acid substitution and applications to mitochondrial protein evolution

Models of amino acid substitution were developed and compared using maximum likelihood. Two kinds of models are considered. "Empirical" models do not explicitly consider factors that shape protein evolution, but attempt to summarize the substitution pattern from large quantities of real data. "Mechanistic" models are formulated at the codon level and separate mutational biases at the nucleotide level from selective constraints at the amino acid level. They account for features of sequence evolution, such as transition-transversion bias and base or codon frequency biases, and make use of physicochemical distances between amino acids to specify nonsynonymous substitution rates. A general approach is presented that transforms a Markov model of codon substitution into a model of amino acid replacement. Protein sequences from the entire mitochondrial genomes of 20 mammalian species were analyzed using different models. The mechanistic models were found to fit the data better than empirical models derived from large databases. Both the mutational distance between amino acids (determined by the genetic code and mutational biases such as the transition-transversion bias) and the physicochemical distance are found to have strong effects on amino acid substitution rates. A significant proportion of amino acid substitutions appeared to have involved more than one codon position, indicating that nucleotide substitutions at neighboring sites may be correlated. Rates of amino acid substitution were found to be highly variable among sites.      

A combined empirical and mechanistic codon model

The evolutionary selection forces acting on a protein are commonly inferred using evolutionary codon models by contrasting the rate of synonymous to nonsynonymous substitutions. Most widely used models are based on theoretical assumptions and ignore the empirical observation that distinct amino acids differ in their replacement rates. In this paper, we develop a general method that allows assimilation of empirical amino acid replacement probabilities into a codon-substitution matrix. In this way, the resulting codon model takes into account not only the transition-transversion bias and the nonsynonymous/synonymous ratio, but also the different amino acid replacement probabilities as specified in empirical amino acid matrices. Different empirical amino acid replacement matrices, such as secondary structure-specific matrices or organelle-specific matrices (e.g., mitochondria and chloroplasts), can be incorporated into the model, making it context dependent. Using a diverse set of coding DNA sequences, we show that the novel model better fits biological data as compared with either mechanistic or empirical codon models. Using the suggested model, we further analyze human immunodeficiency virus type 1 protease sequences obtained from drug-treated patients and reveal positive selection in sites that are known to confer drug resistance to the virus.     

Advantages of a mechanistic codon substitution model for evolutionary analysis of protein-coding sequences

BACKGROUND: A mechanistic codon substitution model, in which each codon substitution rate is proportional to the product of a codon mutation rate and the average fixation probability depending on the type of amino acid replacement, has advantages over nucleotide, amino acid, and empirical codon substitution models in evolutionary analysis of protein-coding sequences. It can approximate a wide range of codon substitution processes. If no selection pressure on amino acids is taken into account, it will become equivalent to a nucleotide substitution model. If mutation rates are assumed not to depend on the codon type, then it will become essentially equivalent to an amino acid substitution model. Mutation at the nucleotide level and selection at the amino acid level can be separately evaluated. RESULTS: The present scheme for single nucleotide mutations is equivalent to the general time-reversible model, but multiple nucleotide changes in infinitesimal time are allowed. Selective constraints on the respective types of amino acid replacements are tailored to each gene in a linear function of a given estimate of selective constraints. Their good estimates are those calculated by maximizing the respective likelihoods of empirical amino acid or codon substitution frequency matrices. Akaike and Bayesian information criteria indicate that the present model performs far better than the other substitution models for all five phylogenetic trees of highly-divergent to highly-homologous sequences of chloroplast, mitochondrial, and nuclear genes. It is also shown that multiple nucleotide changes in infinitesimal time are significant in long branches, although they may be caused by compensatory substitutions or other mechanisms. The variation of selective constraint over sites fits the datasets significantly better than variable mutation rates, except for 10 slow-evolving nuclear genes of 10 mammals. An critical finding for phylogenetic analysis is that assuming variable mutation rates over sites lead to the overestimation of branch lengths.     

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes.     

An empirical codon model for protein sequence evolution

In the past, 2 kinds of Markov models have been considered to describe protein sequence evolution. Codon-level models have been mechanistic with a small number of parameters designed to take into account features, such as transition-transversion bias, codon frequency bias, and synonymous-nonsynonymous amino acid substitution bias. Amino acid models have been empirical, attempting to summarize the replacement patterns observed in large quantities of data and not explicitly considering the distinct factors that shape protein evolution. We have estimated the first empirical codon model (ECM). Previous codon models assume that protein evolution proceeds only by successive single nucleotide substitutions, but our results indicate that model accuracy is significantly improved by incorporating instantaneous doublet and triplet changes. We also find that the affiliations between codons, the amino acid each encodes and the physicochemical properties of the amino acids are main factors driving the process of codon evolution. Neither multiple nucleotide changes nor the strong influence of the genetic code nor amino acids' physicochemical properties form a part of standard mechanistic models and their views of how codon evolution proceeds. We have implemented the ECM for likelihood-based phylogenetic analysis, and an assessment of its ability to describe protein evolution shows that it consistently outperforms comparable mechanistic codon models. We point out the biological interpretation of our ECM and possible consequences for studies of selection.     

Amino acid and codon usage profiles: adaptive changes in the frequency of amino acids and codons

In the work presented, the changes in codon and amino acid contents have been studied as a function of environmental conditions by comparing pairs of homologs in a group of extremophilic/non-extremophilic genomes. Our results obtained based on such analysis highlights a number of notable observations: (i) the overall preference of amino acid usages in the proteins of a given organism is significantly affected by major environmental factors. The changes in amino acid preferences (amino acid usage profiles) in an extremophile compared to its non-extremophile relative recurs in the organisms of similar extreme habitats. (ii) On the other hand, changes in codon usage preferences in these extremophilic/non-extremophilic pairs, lack such persistency not only in different genome-pairs but also in the individual genes of a specific pair. (iii) We have noted a correlation between cellular function and codon usage profiles of the genes in the studied pairs. (iv) Based on this correlation, we could obtain a decent prediction of cellular functions solely based on codon usage profile data. (v) Comparisons made between two sets of randomly generated genomes suggest that different patterns of codon usage changes in genes of different functional categories result in a partial resistance towards the changes in the concentration of a given amino acid. This buffering capacity might explain the observed differences in codon usage trends in genes of different functions. In the end, we suggest codon usage and amino acid profiles as powerful tools that can be utilized to improve function predictions and genome-environment mappings.     

A new semiempirical codon substitution model based on principal component analysis of mammalian sequences

Codon substitution models have traditionally been parametric Markov models, but recently, empirical and semiempirical models also have been proposed. Parametric codon models are typically based on 61×61 rate matrices that are derived from a small number of parameters. These parameters are rooted in experience and theoretical considerations and generally show good performance but are still relatively arbitrary. We have previously used principal component analysis (PCA) on data obtained from mammalian sequence alignments to empirically identify the most relevant parameters for codon substitution models, thereby confirming some commonly used parameters but also suggesting new ones. Here, we present a new semiempirical codon substitution model that is directly based on those PCA results. The substitution rate matrix is constructed from linear combinations of the first few (the most important) principal components with the coefficients being free model parameters. Thus, the model is not only based on empirical rates but also uses the empirically determined most relevant parameters for a codon model to adjust to the particularities of individual data sets. In comparisons against established parametric and semiempirical models, the new model consistently achieves the highest likelihood values when applied to sequences of vertebrates, which include the taxonomic class where the model was trained on.     

An analysis of determinants of amino acids substitution rates in bacterial proteins

The variation of amino acid substitution rates in proteins depends on several variables. Among these, the protein's expression level, functional category, essentiality, or metabolic costs of its amino acid residues may play an important role. However, the relative importance of each variable has not yet been evaluated in comparative analyses. To this aim, we made regression analyses combining data available on these variables and on evolutionary rates, in two well-documented model bacteria, Escherichia coli and Bacillus subtilis. In both bacteria, the level of expression of the protein in the cell was by far the most important driving force constraining the amino acids substitution rate. Subsequent inclusion in the analysis of the other variables added little further information. Furthermore, when the rates of synonymous substitutions were included in the analysis of the E. coli data, only the variable expression levels remained statistically significant. The rate of nonsynonymous substitution was shown to correlate with expression levels independently of the rate of synonymous substitution. These results suggest an important direct influence of expression levels, or at least codon usage bias for translation optimization, on the rates of nonsynonymous substitutions in bacteria. They also indicate that when a control for this variable is included, essentiality plays no significant role in the rate of protein evolution in bacteria, as is the case in eukaryotes.     

Testing for covarion-like evolution in protein sequences

The covarion hypothesis of molecular evolution proposes that selective pressures on an amino acid or nucleotide site change through time, thus causing changes of evolutionary rate along the edges of a phylogenetic tree. Several kinds of Markov models for the covarion process have been proposed. One model, proposed by Huelsenbeck (2002), has 2 substitution rate classes: the substitution process at a site can switch between a single variable rate, drawn from a discrete gamma distribution, and a zero invariable rate. A second model, suggested by Galtier (2001), assumes rate switches among an arbitrary number of rate classes but switching to and from the invariable rate class is not allowed. The latter model allows for some sites that do not participate in the rate-switching process. Here we propose a general covarion model that combines features of both models, allowing evolutionary rates not only to switch between variable and invariable classes but also to switch among different rates when they are in a variable state. We have implemented all 3 covarion models in a maximum likelihood framework for amino acid sequences and tested them on 23 protein data sets. We found significant likelihood increases for all data sets for the 3 models, compared with a model that does not allow site-specific rate switches along the tree. Furthermore, we found that the general model fit the data better than the simpler covarion models in the majority of the cases, highlighting the complexity in modeling the covarion process. The general covarion model can be used for comparing tree topologies, molecular dating studies, and the investigation of protein adaptation.     

Molecular cloning and nucleotide sequence of cDNA for murine senile amyloid protein: nucleotide substitutions found in apolipoprotein A-II cDNA of senescence accelerated mouse (SAM).

cDNA clones encoding the murine senile amyloid protein (ASSAM) have been isolated from animal models of accelerated senescence (SAM-P/1) and from normal aging (SAM-R/1). Immunochemical and protein sequence studies revealed that apolipoprotein (apo) A-II is a serum precursor of ASSAM. A 17-base synthetic oligonucleotide based on residues 39-44 of ASSAM was used as a hybridization probe for screening newly constructed SAM-P/1 and SAM-R/1 liver cDNA libraries. The structure of murine apo A-II cDNA is of interest because of the amino acid substitution found in ASSAM and serum apo A-II of SAM-P; in SAM-R or other random bred slc:ICR mice, amino acid residue 5 of mature apo A-II is proline but, in SAM-P, this amino acid is changed to glutamine. This amino acid replacement is caused by two nucleotide substitutions (CCA for proline codon to CAG for glutamine codon). The third base mutation may not be relevant to the substitution of amino acid. Attention is directed to the relation of this amino acid substitution to the specific deposition of apo A-II, as a tissue amyloid fibril.     

Molecular evolution of the ent-kaurenoic acid oxidase gene in Oryzeae

We surveyed the substitution patterns in the ent-kaurenoic acid oxidase (KAO) gene in 11 species of Oryzeae with an outgroup in the Ehrhartoidaea. The synonymous and non-synonymous substitution rates showed a high positive correlation with each other, but were negatively correlated with codon usage bias and GC content at third codon positions. The substitution rate was heterogenous among lineages. Likelihood-ratio tests showed that the non-synonymous/synonymous rate ratio changed significantly among lineages. Site-specific models provided no evidence for positive selection of particular amino acid sites in any codon of the KAO gene. This finding suggested that the significant rate heterogeneity among some lineages may have been caused by variability in the relaxation of the selective constraint among lineages or by neutral processes.     

Evolution of Ig DNA sequence to target specific base positions within codons for somatic hypermutation

Ig variable (V) region genes are subjected to a somatic hypermutation process as B lymphocytes participate in immune reactions to protein Ags. Although little is known regarding the mechanism of mutagenesis, a consistent hierarchy of trinucleotide target preferences is evident. Analysis of trinucleotide regional distributions predicted and we now empirically confirm the surprising finding that the framework 2 region of kappa V region genes is highly mutable despite its importance to the structural integrity and function of the Ab molecule. Interestingly, much of this mutability appears to be focused on the third codon position where synonymous substitutions are most likely to occur. We also observed a trend for high predicted mutability for codon positions 1 and 2 in complementarity-determining regions. Consequently, amino acid replacements should occur at a higher rate in complementarity-determining regions than in framework regions due to the distribution and subsequent targeting of microsequences by the mutation mechanism. Our results reveal a subtle tier of V region gene evolution in which DNA sequence has been molded to direct mutations to specific base positions within codons in a manner that minimizes damage and maximizes the benefits of the somatic hypermutation process.     

Phylogenetic estimation of context-dependent substitution rates by maximum likelihood

Nucleotide substitution in both coding and noncoding regions is context-dependent, in the sense that substitution rates depend on the identity of neighboring bases. Context-dependent substitution has been modeled in the case of two sequences and an unrooted phylogenetic tree, but it has only been accommodated in limited ways with more general phylogenies. In this article, extensions are presented to standard phylogenetic models that allow for better handling of context-dependent substitution, yet still permit exact inference at reasonable computational cost. The new models improve goodness of fit substantially for both coding and noncoding data. Considering context dependence leads to much larger improvements than does using a richer substitution model or allowing for rate variation across sites, under the assumption of site independence. The observed improvements appear to derive from three separate properties of the models: their explicit characterization of context-dependent substitution within N-tuples of adjacent sites, their ability to accommodate overlapping N-tuples, and their rich parameterization of the substitution process. Parameter estimation is accomplished using an expectation maximization algorithm, with a quasi-Newton algorithm for the maximization step; this approach is shown to be preferable to ordinary Newton methods for parameter-rich models. Overlapping tuples are efficiently handled by assuming Markov dependence of the observed bases at each site on those at the N - 1 preceding sites, and the required conditional probabilities are computed with an extension of Felsenstein's algorithm. Estimated substitution rates based on a data set of about 160,000 noncoding sites in mammalian genomes indicate a pronounced CpG effect, but they also suggest a complex overall pattern of context-dependent substitution, comprising a variety of subtle effects. Estimates based on about 3 million sites in coding regions demonstrate that amino acid substitution rates can be learned at the nucleotide level, and suggest that context effects across codon boundaries are significant.     

Synonymous substitutions substantially improve evolutionary inference from highly diverged proteins

Codon-and amino acid-substitution models are widely used for the evolutionary analysis of protein-coding DNA sequences. Using codon models, the amounts of both nonsynonymous and synonymous DNA substitutions can be estimated. The ratio of these amounts represents the strength of selective pressure. Using amino acid models, the amount of nonsynonymous substitutions is estimated, but that of synonymous substitutions is ignored. Although amino acid models lose any information regarding synonymous substitutions, they explicitly incorporate the information for amino acid replacement, which is empirically derived from databases. It is often presumed that when the protein-coding sequences are highly divergent, synonymous substitutions might be saturated and the evolutionary analysis may be hampered by synonymous noise. However, there exists no quantitative procedure to verify whether synonymous substitutions can be ignored; therefore, amino acid models have been arbitrarily selected. In this study, we investigate the issue of a statistical comparison between codon-and amino acid-substitution models. For this purpose, we propose a new procedure to transform a 20-dimensional amino acid model to a 61-dimensional codon model. This transformation reveals that amino acid models belong to a subset of the codon models and enables us to test whether synonymous substitutions can be ignored by using the likelihood ratio. Our theoretical results and analyses of real data indicate that synonymous substitutions are very informative and substantially improve evolutionary inference, even when the sequences are highly divergent. Therefore, we note that amino acid models should be adopted only after carefully investigating and discarding the possibility that synonymous substitutions can reveal important evolutionary information.     

Challenges of the genetic code for exploring sequence space in directed protein evolution

Directed protein evolution is the most versatile method for studying protein structure-function relationships, and for tailoring a protein's properties to the needs of industrial applications. In this review, we performed a statistical analysis on the genetic code to study the extent and consequence of the organization of the genetic code on amino acid substitution patterns generated in directed evolution experiments. In detail, we analyzed amino acid substitution patterns caused by (a) a single nucleotide (nt) exchange at each position of all 64 codons, and (b) two subsequent nt exchanges (first and second nt, first and third nt, second and third nt). Additionally, transitions and transversions mutations were compared at the level of amino acid substitution patterns. The latter analysis showed that single nucleotide substitution in a codon generates only 39.5% of the natural diversity on the protein level with 5.2-7 amino acid substitutions per codon. Transversions generate more complex amino acid substitution patterns (increased number and chemically more diverse amino acid substitutions) than transitions. Simultaneous nt exchanges at both first and second nt of a codon generates very diverse amino acid substitution patterns, achieving 83.2% of the natural diversity. The statistical analysis described in this review sets the objectives for novel random mutagenesis methods that address the consequences of the organization of the genetic code. Random mutagenesis methods that favor transversions or introduce consecutive nt exchanges can contribute in this regard.     

Challenges of the genetic code for exploring sequence space in directed protein evolution

Directed protein evolution is the most versatile method for studying protein structure–function relationships, and for tailoring a protein's properties to the needs of industrial applications. In this review, we performed a statistical analysis on the genetic code to study the extent and consequence of the organization of the genetic code on amino acid substitution patterns generated in directed evolution experiments. In detail, we analyzed amino acid substitution patterns caused by (a) a single nucleotide (nt) exchange at each position of all 64 codons, and (b) two subsequent nt exchanges (first and second nt, first and third nt, second and third nt). Additionally, transitions and transversions mutations were compared at the level of amino acid substitution patterns. The latter analysis showed that single nucleotide substitution in a codon generates only 39.5% of the natural diversity on the protein level with 5.2–7 amino acid substitutions per codon. Transversions generate more complex amino acid substitution patterns (increased number and chemically more diverse amino acid substitutions) than transitions. Simultaneous nt exchanges at both first and second nt of a codon generates very diverse amino acid substitution patterns, achieving 83.2% of the natural diversity. The statistical analysis described in this review sets the objectives for novel random mutagenesis methods that address the consequences of the organization of the genetic code. Random mutagenesis methods that favor transversions or introduce consecutive nt exchanges can contribute in this regard.