Significant impact of protein dispensability on the instantaneous rate of protein evolution

The neutral theory of molecular evolution predicts that important proteins evolve more slowly than unimportant ones. High-throughput gene-knockout experiments in model organisms have provided information on the dispensability, and therefore importance, of thousands of proteins in a genome. However, previous studies of the correlation between protein dispensability and evolutionary rate were equivocal, and it has been proposed that the observed correlation is due to the covariation with the level of gene expression or is limited to duplicate genes. We here analyzed the gene dispensability data of the yeast Saccharomyces cerevisiae and estimated protein evolutionary rates by comparing S. cerevisiae with nine species of varying degrees of divergence from S. cerevisiae. The correlation between gene dispensability and evolutionary rate, although low, is highly significant, even when the gene expression level is controlled for or when duplicate genes are excluded. Our results thus support the hypothesis of lower evolution rates for more important proteins, a widely used principle in the daily practice of molecular biology. When the evolutionary rate is estimated from closely related species, the ratio between the mean rate of nonessential proteins to that of essential proteins is 1.4. This ratio declines to 1.1 when the evolutionary rate is estimated from distantly related species, suggesting that the importance of a protein may change in evolution, so the dispensability data obtained from a model organism only predicts a short-term rate of protein evolution. A comparison of the fitness contributions of orthologous genes in yeast and nematode supports this conclusion.     

Parasitism as a constraint on the rate of life-history evolution

There are a number of ways in which a host can respond in evolutionary time to reductions in survival and reproduction due to a virulent parasite. These include evolving physiological morphological, or behavioural mechanisms of resistance to infection (or to proliferation, once infection has occurred). But a more unexpected tactic is also possible. This is for hosts to reproduce (slightly) sooner when in the presence of a virulent parasite as compared to when the parasite is less virulent or absent. As such, hosts which reproduce younger may be at a selective advantage, since they can both evade parasitism in time and, even when parasitised, can reduce the likely impact of the parasite on survival and reproductive success. We employ a simple mathematical model to propose that parasites and pathogens can act as important agents in the evolution of the timing of reproduction and associated life-history characters (e.g. body size). Once established in a semelparous host population, evolutionary increases in parasite virulence should result in the evolution of shorter lived hosts; whereas the evolution of less virulent forms of the parasite should be accompanied by the evolution of longer lived hosts. We argue that in the presence of a sufficiently virulent parasite the evolution of longer pre-reproductive life-spans should require the previous or concomitant evolution of morphological, behavioural or physiological resistance to parasitic infection and proliferation.     

A single determinant dominates the rate of yeast protein evolution

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.     

Speculations on the evolution of the genetic code IV the evolution of the aminoacyl-tRNA synthetases

An evolutionary scheme is postulated in which a primitive code, involving only guanine and cytosine, would code for glycine(GG.), alanine(GC), arginine(CG.) and proline(CC). There evolves from this primitive code families of related amino acids as the code expands. The evolution of the aminiacyl-tRNA synthetases are considered to be indicators for the evolution of the genetic code. The postulated model for the evolution of the genetic code is used to give an evolutionary interpretation to the recent work on the structure and sequences of the aminoacyl-tRNA synthetases.     

Effects of metabolic rate on protein evolution

Since the modern evolutionary synthesis was first proposed early in the twentieth century, attention has focused on assessing the relative contribution of mutation versus natural selection on protein evolution. Here we test a model that yields general quantitative predictions on rates of protein evolution by combining principles of individual energetics with Kimura's neutral theory. The model successfully predicts much of the heterogeneity in rates of protein evolution for diverse eukaryotes (i.e. fishes, amphibians, reptiles, birds, mammals) from different thermal environments. Data also show that the ratio of non-synonymous to synonymous nucleotide substitution is independent of body size, and thus presumably of effective population size. These findings indicate that rates of protein evolution are largely controlled by mutation rates, which in turn are strongly influenced by individual metabolic rate.     

HIV-specific probabilistic models of protein evolution

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1-the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses.     

Time scale of eutherian evolution estimated without assuming a constant rate of molecular evolution

Controversies over the molecular clock hypothesis were reviewed. Since it is evident that the molecular clock does not hold in an exact sense, accounting for evolution of the rate of molecular evolution is a prerequisite when estimating divergence times with molecular sequences. Recently proposed statistical methods that account for this rate variation are overviewed and one of these procedures is applied to the mitochondrial protein sequences and to the nuclear gene sequences from many mammalian species in order to estimate the time scale of eutherian evolution. This Bayesian method not only takes account of the variation of molecular evolutionary rate among lineages and among genes, but it also incorporates fossil evidence via constraints on node times. With denser taxonomic sampling and a more realistic model of molecular evolution, this Bayesian approach is expected to increase the accuracy of divergence time estimates.     

Gene finding with a hidden Markov model of genome structure and evolution

Motivation: A growing number of genomes are sequenced. The differences in evolutionary pattern between functional regions can thus be observed genome-wide in a whole set of organisms. The diverse evolutionary pattern of different functional regions can be exploited in the process of genomic annotation. The modelling of evolution by the existing comparative gene finders leaves room for improvement. Results: A probabilistic model of both genome structure and evolution is designed. This type of model is called an Evolutionary Hidden Markov Model (EHMM), being composed of an HMM and a set of region-specific evolutionary models based on a phylogenetic tree. All parameters can be estimated by maximum likelihood, including the phylogenetic tree. It can handle any number of aligned genomes, using their phylogenetic tree to model the evolutionary correlations. The time complexity of all algorithms used for handling the model are linear in alignment length and genome number. The model is applied to the problem of gene finding. The benefit of modelling sequence evolution is demonstrated both in a range of simulations and on a set of orthologous human/mouse gene pairs. AVAILABILITY: Free availability over the Internet on www server: http://www.birc.dk/Software/evogene.     

Predicting evolutionary potential: in vitro evolution accurately reproduces natural evolution of the tem beta-lactamase

To evaluate the validity of our in vitro evolution method as a model for natural evolutionary processes, the TEM-1 beta-lactamase gene was evolved in vitro and was selected for increased resistance to cefotaxime, cefuroxime, ceftazadime, and aztreonam, i.e., the "extended-spectrum" phenotype. The amino acid substitutions recovered in 10 independent in vitro evolvants were compared with the amino acid substitutions in the naturally occurring extended-spectrum TEM alleles. Of the nine substitutions that have arisen multiple times in naturally occurring extended-spectrum TEM alleles, seven were recovered multiple times in vitro. We take this result as evidence that our in vitro evolution technique accurately mimics natural evolution and can therefore be used to predict the results of natural evolutionary processes. Additionally, our results predict that a phenotype not yet observed among TEM beta-lactamases in nature-resistance to cefepime-is likely to arise in nature.     

Protein evolution rate and immunoglobulin induction

The capacity of proteins to induce the synthesis of specific immunoglobulins was shown to be correlated with their evolution rate. This correlation can be understood in terms of the clonal-selectional theory. The characteristic correlation parameter is the number of differences in the amino acid sequences between the immunogenic protein and the homological protein of the immunized animal. The above correlation was traced most clearly for the evolutionary conservative proteins.     

The evolution of outsider exclusion

The rate of migration between different parts of a population can be important in determining evolutionary outcomes. This paper presents a mathematical model in which some individuals act to exclude immigrants from their group. It is shown that outsider exclusion can be favoured by evolution, even when outsider excluders incur a large cost. In addition, it is shown that the evolutionary mechanism which causes increases in the frequency of outsider excluders is a form of kin selection or group selection. A second model shows that a similar mechanisms can act to favour the evolution of reluctance to mate with immigrants.     

Comparable contributions of structural-functional constraints and expression level to the rate of protein sequence evolution

Background  

Proteins show a broad range of evolutionary rates. Understanding the factors that are responsible for the characteristic rate of evolution of a given protein arguably is one of the major goals of evolutionary biology. A long-standing general assumption used to be that the evolution rate is, primarily, determined by the specific functional constraints that affect the given protein. These constrains were traditionally thought to depend both on the specific features of the protein's structure and its biological role. The advent of systems biology brought about new types of data, such as expression level and protein-protein interactions, and unexpectedly, a variety of correlations between protein evolution rate and these variables have been observed. The strongest connections by far were repeatedly seen between protein sequence evolution rate and the expression level of the respective gene. It has been hypothesized that this link is due to the selection for the robustness of the protein structure to mistranslation-induced misfolding that is particularly important for highly expressed proteins and is the dominant determinant of the sequence evolution rate.     

The evolution of multicomponent mimicry

The relative sizes of phenotypic mutations contributing to evolutionary change has long been the subject of debate. We describe how mimicry research can shed light on this debate, and frame mimicry studies within the general context of macromutationism and micromutationism, and punctuated versus gradual evolution. Balogh and Leimar [Müllerian mimicry: an examination of Fisher's theory of gradual evolutionary change. Proc. Roy. Soc. Lond. B Biol. Sci. 272, 2269-2275] have recently used a model to readdress the question of whether or not mimicry evolves gradually along a single dimension. We extend their approach, and present the first model to consider the effect of predator generalization along multiple components on the evolution of mimicry. We find that the gradual evolution of mimicry becomes increasingly less likely as the number of signal components increases, unless predators generalize widely over all components. However, we show that the contemporary two-step hypothesis (punctuated evolution followed by gradual refinement) can explain the evolution of Müllerian mimicry under all tested conditions. Thus, although the gradual evolution of mimicry is possible, the two-step hypothesis appears more generally applicable.     

The impact of rate heterogeneity on inference of phylogenetic models of trait evolution

Rates of trait evolution are known to vary across phylogenies; however, standard evolutionary models assume a homogeneous process of trait change. These simple methods are widely applied in small‐scale phylogenetic studies, whereas models of rate heterogeneity are not, so the prevalence and patterns of potential rate variation in groups up to hundreds of species remain unclear. The extent to which trait evolution is modelled accurately on a given phylogeny is also largely unknown because studies typically lack absolute model fit tests. We investigated these issues by applying both rate‐static and variable‐rates methods on (i) body mass data for 88 avian clades of 10–318 species, and (ii) data simulated under a range of rate‐heterogeneity scenarios. Our results show that rate heterogeneity is present across small‐scaled avian clades, and consequently applying only standard single‐process models prompts inaccurate inferences about the generating evolutionary process. Specifically, these approaches underestimate rate variation, and systematically mislabel temporal trends in trait evolution. Conversely, variable‐rates approaches have superior relative fit (they are the best model) and absolute fit (they describe the data well). We show that rate changes such as single internal branch variations, rate decreases and early bursts are hard to detect, even by variable‐rates models. We also use recently developed absolute adequacy tests to highlight misleading conclusions based on relative fit alone (e.g. a consistent preference for constrained evolution when isolated terminal branch rate increases are present). This work highlights the potential for robust inferences about trait evolution when fitting flexible models in conjunction with tests for absolute model fit.     

Experimental evolution

Experimental evolution is the study of evolutionary processes occurring in experimental populations in response to conditions imposed by the experimenter. This research approach is increasingly used to study adaptation, estimate evolutionary parameters, and test diverse evolutionary hypotheses. Long applied in vaccine development, experimental evolution also finds new applications in biotechnology. Recent technological developments provide a path towards detailed understanding of the genomic and molecular basis of experimental evolutionary change, while new findings raise new questions that can be addressed with this approach. However, experimental evolution has important limitations, and the interpretation of results is subject to caveats resulting from small population sizes, limited timescales, the simplified nature of laboratory environments, and, in some cases, the potential to misinterpret the selective forces and other processes at work.     

Feature saltation and the evolution of mimicry

In Batesian mimicry, a harmless prey species imitates the warning coloration of an unpalatable model species. A traditional suggestion is that mimicry evolves in a two-step process, in which a large mutation first achieves approximate similarity to the model, after which smaller changes improve the likeness. However, it is not known which aspects of predator psychology cause the initial mutant to be perceived by predators as being similar to the model, leaving open the question of how the crucial first step of mimicry evolution occurs. Using theoretical evolutionary simulations and reconstruction of examples of mimicry evolution, we show that the evolution of Batesian mimicry can be initiated by a mutation that causes prey to acquire a trait that is used by predators as a feature to categorize potential prey as unsuitable. The theory that species gain entry to mimicry through feature saltation allows us to formulate scenarios of the sequence of events during mimicry evolution and to reconstruct an initial mimetic appearance for important examples of Batesian mimicry. Because feature-based categorization by predators entails a qualitative distinction between nonmimics and passable mimics, the theory can explain the occurrence of imperfect mimicry.     

Simulation of the evolution of genomic complexity.

A general evolutionary trend is the generation of organisms of increasing complexity, notwithstanding that reduction and simplification phenomena do occur in the evolutionary process. This paper proposes an evolutionary model incorporating the mechanisms of gene amplification and deletion. The evolutionary process leading to genomic complexity and the coexistence of simpler organisms with complicated ones were both simulated using the proposed model. The model was also used to investigate the influence of various factors on the evolution of complexity. The simulations indicated that the evolution of complexity is largely influenced by adaptation to complicated environments. Nevertheless, complex organisms require relatively more resources for survival and replication, which limits the on going tendency towards complexity. Moreover, the analysis showed that if the environment varies rapidly and the profit obtained from complexity is greater than the resources consumed, selection will tend to favor complexity. However, high living cost will tend to limit the trend of complexity and if the environment is relatively stable, reduction and simplification will become the dominant trends.     

The evolution of mutation rate in finite asexual populations

In this article, we model analytically the evolution of mutation rate in asexual organisms. Three selective forces are present. First, everything else being equal, individuals with higher mutation rate have a larger fitness, thanks to the energy and time saved by not replicating DNA accurately. Second, as a flip side, the genome of these individuals is replicated with errors that may negatively affect fitness. Third, and conversely, replication errors have a potential benefit if beneficial mutations are to be generated. Our model describes the fate of modifiers of mutation rate under the three forces and allows us to predict the long-term evolutionary trajectory of mutation rate. We obtain three major results. First, in asexuals, the needs for both adaptation and genome preservation are not evolutionary forces that can stabilize mutation rate at an intermediate optimum. When adaptation has a significant role, it primarily destabilizes mutation rate and yields the emergence of strong-effect mutators. Second, in contrast to what is usually believed, the appearance of modifiers with large mutation rate is more likely when the fitness cost of each deleterious mutation is weak, because the cost of replication errors is then paid after a delay. Third, in small populations, and even if adaptations are needed, mutation rate is always blocked at the minimum attainable level, because the rate of adaptation is too slow to play a significant role. Only populations whose size is above a critical mass see their mutation rate affected by the need for adaptation.     

The nearly neutral and selection theories of molecular evolution under the fisher geometrical framework: substitution rate, population size, and complexity

The general theories of molecular evolution depend on relatively arbitrary assumptions about the relative distribution and rate of advantageous, deleterious, neutral, and nearly neutral mutations. The Fisher geometrical model (FGM) has been used to make distributions of mutations biologically interpretable. We explored an FGM-based molecular model to represent molecular evolutionary processes typically studied by nearly neutral and selection models, but in which distributions and relative rates of mutations with different selection coefficients are a consequence of biologically interpretable parameters, such as the average size of the phenotypic effect of mutations and the number of traits (complexity) of organisms. A variant of the FGM-based model that we called the static regime (SR) represents evolution as a nearly neutral process in which substitution rates are determined by a dynamic substitution process in which the population's phenotype remains around a suboptimum equilibrium fitness produced by a balance between slightly deleterious and slightly advantageous compensatory substitutions. As in previous nearly neutral models, the SR predicts a negative relationship between molecular evolutionary rate and population size; however, SR does not have the unrealistic properties of previous nearly neutral models such as the narrow window of selection strengths in which they work. In addition, the SR suggests that compensatory mutations cannot explain the high rate of fixations driven by positive selection currently found in DNA sequences, contrary to what has been previously suggested. We also developed a generalization of SR in which the optimum phenotype can change stochastically due to environmental or physiological shifts, which we called the variable regime (VR). VR models evolution as an interplay between adaptive processes and nearly neutral steady-state processes. When strong environmental fluctuations are incorporated, the process becomes a selection model in which evolutionary rate does not depend on population size, but is critically dependent on the complexity of organisms and mutation size. For SR as well as VR we found that key parameters of molecular evolution are linked by biological factors, and we showed that they cannot be fixed independently by arbitrary criteria, as has usually been assumed in previous molecular evolutionary models.     

Adaptive dynamics in diploid, sexual populations and the evolution of reproductive isolation

Evolutionary branching is the process whereby an initially monomorphic population evolves to a point where it undergoes disruptive selection and splits up into two phenotypically diverging lineages. We studied evolutionary branching in three models that are ecologically identical but that have different genetic systems. The first model is clonal, the second is sexual diploid with additive genetics on a single locus and the third is like the second but with an additional locus for mate choice. Evolutionary branching occurred under exactly the same ecological circumstances in all three models. After branching the evolutionary dynamics may be qualitatively different. In particular, in the diploid, sexual models there can be multiple evolutionary outcomes whereas in the corresponding clonal model there is only one. We showed that evolutionary branching favours the evolution of (partial) assortative mating and that this in turn effectively restores the results from the clonal model by rendering the alternative outcomes unreachable except for the one that also occurs in the clonal model. The evolution of assortative mating during evolutionary branching can be interpreted as the initial phase of sympatric speciation with phenotypic divergence and partial reproductive isolation.