Hiroshi Akashi

Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis.

Biosynthesis of an Escherichia coli cell, with organic compounds as sources of energy and carbon, requires approximately 20 to 60 billion high-energy phosphate bonds [Stouthamer, A. H. (1973) Antonie van Leeuwenhoek 39, 545–565]. A substantial fraction of this energy budget is devoted to biosynthesis of amino acids, the building blocks of proteins. The fueling reactions of central metabolism provide precursor metabolites for synthesis of the 20 amino acids incorporated into proteins. Thus, synthesis of an amino acid entails a dual cost: energy is lost by diverting chemical intermediates from fueling reactions and additional energy is required to convert precursor metabolites to amino acids. Among amino acids, costs of synthesis vary from 12 to 74 high-energy phosphate bonds per molecule. The energetic advantage to encoding a less costly amino acid in a highly expressed gene can be greater than 0.025% of the total energy budget. Here, we provide evidence that amino acid composition in the proteomes of E. coli and Bacillus subtilis reflects the action of natural selection to enhance metabolic efficiency. We employ synonymous codon usage bias as a measure of translation rates and show increases in the abundance of less energetically costly amino acids in highly expressed proteins.

Gene expression and molecular evolution

The combination of complete genome sequence information and estimates of mRNA abundances have begun to reveal causes of both silent and protein sequence evolution. Translational selection appears to explain patterns of synonymous codon usage in many prokaryotes as well as a number of eukaryotic model organisms (with the notable exception of vertebrates). Relationships between gene length and codon usage bias, however, remain unexplained. Intriguing correlations between expression patterns and protein divergence suggest some general mechanisms underlying protein evolution.

A test of translational selection at 'silent' sites in the human genome: base composition comparisons in alternatively spliced genes

Natural selection appears to discriminate among synonymous codons to enhance translational efficiency in a wide range of prokaryotes and eukaryotes. Codon bias is strongly related to gene expression levels in these species. In addition, between-gene variation in silent DNA divergence is inversely correlated with codon bias. However, in mammals, between-gene comparisons are complicated by distinctive nucleotide-content bias (isochores) throughout the genome. In this study, we attempted to identify translational selection by analyzing the DNA sequences of alternatively spliced genes in humans and in Drosophila melanogaster. Among codons in an alternatively spliced gene, those in constitutively expressed exons are translated more often than those in alternatively spliced exons. Thus, translational selection should act more strongly to bias codon usage and reduce silent divergence in constitutive than in alternative exons. By controlling for regional forces affecting base-composition evolution, this within-gene comparison makes it possible to detect codon selection at synonymous sites in mammals. We found that GC-ending codons are more abundant in constitutive than alternatively spliced exons in both Drosophila and humans. Contrary to our expectation, however, silent DNA divergence between mammalian species is higher in constitutive than in alternative exons.

Mutation pressure, natural selection, and the evolution of base composition in Drosophila

Genome sequencing in a number of taxa has revealed variation in nucleotide composition both among regions of the genome and among functional classes of sites in DNA. Mutational biases, biased gene conversion, and natural selection have been proposed as causes of this variation. Here, we review patterns of base composition in Drosophila DNA. Nucleotide composition in Drosophila melanogaster varys regionally, and base composition is correlated between introns and exons. Drosophila species also show striking patterns of non-random codon usage. Patterns of synonymous codon usage and the biochemistry of translation suggest that natural selection may act at ‘silent’ sites. A relationship between recombination rates and codon usage and comparisons of the evolutionary dynamics of silent mutations within and between species support natural selection discriminating among synonymous codons. The causes of regional base composition variation are less clear. Progress in functional studies of non-coding DNA, further investigations of genome patterns, and statistical tests based on evolutionary theory will lead to a greater understanding of the contributions of mutational processes and natural selection in patterning genome-wide nucleotide composition.

Weak Selection and Protein Evolution

The “nearly neutral” theory of molecular evolution proposes that many features of genomes arise from the interaction of three weak evolutionary forces: mutation, genetic drift, and natural selection acting at its limit of efficacy. Such forces generally have little impact on allele frequencies within populations from generation to generation but can have substantial effects on long-term evolution. The evolutionary dynamics of weakly selected mutations are highly sensitive to population size, and near neutrality was initially proposed as an adjustment to the neutral theory to account for general patterns in available protein and DNA variation data. Here, we review the motivation for the nearly neutral theory, discuss the structure of the model and its predictions, and evaluate current empirical support for interactions among weak evolutionary forces in protein evolution. Near neutrality may be a prevalent mode of evolution across a range of functional categories of mutations and taxa. However, multiple evolutionary mechanisms (including adaptive evolution, linked selection, changes in fitness-effect distributions, and weak selection) can often explain the same patterns of genome variation. Strong parameter sensitivity remains a limitation of the nearly neutral model, and we discuss concave fitness functions as a plausible underlying basis for weak selection.

Molecular phylogeny of the Drosophila melanogaster species subgroup

Although molecular and phenotypic evolution have been studied extensively in Drosophila melanogaster and its close relatives, phylogenetic relationships within the D. melanogaster species subgroup remain unresolved. In particular, recent molecular studies have not converged on the branching orders of the D. yakuba–D. teissieri and D. erecta–D. orena species pairs relative to the D. melanogaster–D. simulans–D. mauritiana–D. sechellia species complex. Here, we reconstruct the phylogeny of the melanogaster species subgroup using DNA sequence data from four nuclear genes. We have employed “vectorette PCR” to obtain sequence data for orthologous regions of the Alcohol dehydrogenase (Adh), Alcohol dehydrogenase related (Adhr), Glucose dehydrogenase (Gld), and rosy (ry) genes (totaling 7164 bp) from six melanogaster subgroup species (D. melanogaster, D. simulans, D. teissieri, D. yakuba, D. erecta, and D. orena) and three species from subgroups outside the melanogaster species subgroup [D. eugracilis (eugracilis subgroup), D. mimetica (suzukii subgroup), and D. lutescens (takahashii subgroup)]. Relationships within the D. simulans complex are not addressed. Phylogenetic analyses employing maximum parsimony, neighbor-joining, and maximum likelihood methods strongly support a D. yakuba–D. teissieri and D. erecta–D. orena clade within the melanogaster species subgroup. D. eugracilis is grouped closer to the melanogaster subgroup than a D. mimetica–D. lutescens clade. This tree topology is supported by reconstructions employing simple (single parameter) and more complex (nonreversible) substitution models.

Molecular Evolution in the Drosophila melanogaster Species Subgroup: Frequent Parameter Fluctuations on the Timescale of Molecular Divergence

Although mutation, genetic drift, and natural selection are well established as determinants of genome evolution, the importance (frequency and magnitude) of parameter fluctuations in molecular evolution is less understood. DNA sequence comparisons among closely related species allow specific substitutions to be assigned to lineages on a phylogenetic tree. In this study, we compare patterns of codon usage and protein evolution in 22 genes (>11,000 codons) among Drosophila melanogaster and five relatives within the D. melanogaster subgroup. We assign changes to eight lineages using a maximum-likelihood approach to infer ancestral states. Uncertainty in ancestral reconstructions is taken into account, at least to some extent, by weighting reconstructions by their posterior probabilities. Four of the eight lineages show potentially genomewide departures from equilibrium synonymous codon usage; three are decreasing and one is increasing in major codon usage. Several of these departures are consistent with lineage-specific changes in selection intensity (selection coefficients scaled to effective population size) at silent sites. Intron base composition and rates and patterns of protein evolution are also heterogeneous among these lineages. The magnitude of forces governing silent, intron, and protein evolution appears to have varied frequently, and in a lineage-specific manner, within the D. melanogaster subgroup.

Do Amino Acid Biosynthetic Costs Constrain Protein Evolution in Saccharomyces cerevisiae

Prokaryotic organisms preferentially utilize less energetically costly amino acids in highly expressed genes. Studies have shown that the proteome of Saccharomyces cerevisiae also exhibits this behavior, but only in broad terms. This study examines the question of metabolic efficiency as a proteome-shaping force at a finer scale, examining whether trends consistent with cost minimization as an evolutionary force are present independent of protein function and amino acid physicochemical property, and consistently with respect to amino acid biosynthetic costs. Inverse correlations between the average amino acid biosynthetic cost of the protein product and the levels of gene expression in S. cerevisiae are consistent with natural selection to minimize costs. There are, however, patterns of amino acid usage that raise questions about the strength (and possibly the universality) of this selective force in shaping S. cerevisiae’s proteome.

Ancestral Inference and the Study of Codon Bias Evolution: Implications for Molecular Evolutionary Analyses of the Drosophila melanogaster Subgroup

Reliable inference of ancestral sequences can be critical to identifying both patterns and causes of molecular evolution. Robustness of ancestral inference is often assumed among closely related species, but tests of this assumption have been limited. Here, we examine the performance of inference methods for data simulated under scenarios of codon bias evolution within the Drosophila melanogaster subgroup. Genome sequence data for multiple, closely related species within this subgroup make it an important system for studying molecular evolutionary genetics. The effects of asymmetric and lineage-specific substitution rates (i.e., varying levels of codon usage bias and departures from equilibrium) on the reliability of ancestral codon usage was investigated. Maximum parsimony inference, which has been widely employed in analyses of Drosophila codon bias evolution, was compared to an approach that attempts to account for uncertainty in ancestral inference by weighting ancestral reconstructions by their posterior probabilities. The latter approach employs maximum likelihood estimation of rate and base composition parameters. For equilibrium and most non-equilibrium scenarios that were investigated, the probabilistic method appears to generate reliable ancestral codon bias inferences for molecular evolutionary studies within the D. melanogaster subgroup. These reconstructions are more reliable than parsimony inference, especially when codon usage is strongly skewed. However, inference biases are considerable for both methods under particular departures from stationarity (i.e., when adaptive evolution is prevalent). Reliability of inference can be sensitive to branch lengths, asymmetry in substitution rates, and the locations and nature of lineage-specific processes within a gene tree. Inference reliability, even among closely related species, can be strongly affected by (potentially unknown) patterns of molecular evolution in lineages ancestral to those of interest.

Evaluation of Ancestral Sequence Reconstruction Methods to Infer Nonstationary Patterns of Nucleotide Substitution

Inference of gene sequences in ancestral species has been widely used to test hypotheses concerning the process of molecular sequence evolution. However, the approach may produce spurious results, mainly because using the single best reconstruction while ignoring the suboptimal ones creates systematic biases. Here we implement methods to correct for such biases and use computer simulation to evaluate their performance when the substitution process is nonstationary. The methods we evaluated include parsimony and likelihood using the single best reconstruction (SBR), averaging over reconstructions weighted by the posterior probabilities (AWP), and a new method called expected Markov counting (EMC) that produces maximum-likelihood estimates of substitution counts for any branch under a nonstationary Markov model. We simulated base composition evolution on a phylogeny for six species, with different selective pressures on G+C content among lineages, and compared the counts of nucleotide substitutions recorded during simulation with the inference by different methods. We found that large systematic biases resulted from (i) the use of parsimony or likelihood with SBR, (ii) the use of a stationary model when the substitution process is nonstationary, and (iii) the use of the Hasegawa-Kishino-Yano (HKY) model, which is too simple to adequately describe the substitution process. The nonstationary general time reversible (GTR) model, used with AWP or EMC, accurately recovered the substitution counts, even in cases of complex parameter fluctuations. We discuss model complexity and the compromise between bias and variance and suggest that the new methods may be useful for studying complex patterns of nucleotide substitution in large genomic data sets.