Tissue-Specific Evolution of Protein Coding Genes in Human and Mouse

Protein-coding genes evolve at different rates, and the influence of different parameters, from gene size to expression level, has been extensively studied. While in yeast gene expression level is the major causal factor of gene evolutionary rate, the situation is more complex in animals. Here we investigate these relations further, especially taking in account gene expression in different organs as well as indirect correlations between parameters. We used RNA-seq data from two large datasets, covering 22 mouse tissues and 27 human tissues. Over all tissues, evolutionary rate only correlates weakly with levels and breadth of expression. The strongest explanatory factors of purifying selection are GC content, expression in many developmental stages, and expression in brain tissues. While the main component of evolutionary rate is purifying selection, we also find tissue-specific patterns for sites under neutral evolution and for positive selection. We observe fast evolution of genes expressed in testis, but also in other tissues, notably liver, which are explained by weak purifying selection rather than by positive selection.     

Evolution of Gene Regulatory Networks by Fluctuating Selection and Intrinsic Constraints

Various characteristics of complex gene regulatory networks (GRNs) have been discovered during the last decade, e.g., redundancy, exponential indegree distributions, scale-free outdegree distributions, mutational robustness, and evolvability. Although progress has been made in this field, it is not well understood whether these characteristics are the direct products of selection or those of other evolutionary forces such as mutational biases and biophysical constraints. To elucidate the causal factors that promoted the evolution of complex GRNs, we examined the effect of fluctuating environmental selection and some intrinsic constraining factors on GRN evolution by using an individual-based model. We found that the evolution of complex GRNs is remarkably promoted by fixation of beneficial gene duplications under unpredictably fluctuating environmental conditions and that some internal factors inherent in organisms, such as mutational bias, gene expression costs, and constraints on expression dynamics, are also important for the evolution of GRNs. The results indicate that various biological properties observed in GRNs could evolve as a result of not only adaptation to unpredictable environmental changes but also non-adaptive processes owing to the properties of the organisms themselves. Our study emphasizes that evolutionary models considering such intrinsic constraining factors should be used as null models to analyze the effect of selection on GRN evolution.     

No accelerated rate of protein evolution in male-biased Drosophila pseudoobscura genes

Sexually dimorphic traits are often subject to diversifying selection. Genes with a male-biased gene expression also are probably affected by sexual selection and have a high rate of protein evolution. We used SAGE to measure sex-biased gene expression in Drosophila pseudoobscura. Consistent with previous results from D. melanogaster, a larger number of genes were male biased (402 genes) than female biased (138 genes). About 34% of the genes changed the sex-related expression pattern between D. melanogaster and D. pseudoobscura. Combining gene expression with protein divergence between both species, we observed a striking difference in the rate of evolution for genes with a male-biased gene expression in one species only. Contrary to expectations, D. pseudoobscura genes in this category showed no accelerated rate of protein evolution, while D. melanogaster genes did. If sexual selection is driving molecular evolution of male-biased genes, our data imply a radically different selection regime in D. pseudoobscura.     

Signatures of selection and sex-specific expression variation of a novel duplicate during the evolution of the Drosophila desaturase gene family

The tempo and mode of evolution of loci with a large effect on adaptation and reproductive isolation will influence the rate of evolutionary divergence and speciation. Desaturase loci are involved in key biochemical changes in long-chain fatty acids. In insects, these have been shown to influence adaptation to starvation or desiccation resistance and in some cases act as important pheromones. The desaturase gene family of Drosophila is known to have evolved by gene duplication and diversification, and at least one locus shows rapid evolution of sex-specific expression variation. Here, we examine the evolution of the gene family in species representing the Drosophila phylogeny. We find that the family includes more loci than have been previously described. Most are represented as single-copy loci, but we also find additional examples of duplications in loci which influence pheromone blends. Most loci show patterns of variation associated with purifying selection, but there are strong signatures of diversifying selection in new duplicates. In the case of a new duplicate of desat1 in the obscura group species, we show that strong selection on the coding sequence is associated with the evolution of sex-specific expression variation. It seems likely that both sexual selection and ecological adaptation have influenced the evolution of this gene family in Drosophila.     

Evaluating the role of natural selection in the evolution of gene regulation

Surveys of gene expression reveal extensive variability both within and between a wide range of species. Compelling cases have been made for adaptive changes in gene regulation, but the proportion of expression divergence attributable to natural selection remains unclear. Distinguishing adaptive changes driven by positive selection from neutral divergence resulting from mutation and genetic drift is critical for understanding the evolution of gene expression. Here, we review the various methods that have been used to test for signs of selection in genomic expression data. We also discuss properties of regulatory systems relevant to neutral models of gene expression. Despite some potential caveats, published studies provide considerable evidence for adaptive changes in gene expression. Future challenges for studies of regulatory evolution will be to quantify the frequency of adaptive changes, identify the genetic basis of expression divergence and associate changes in gene expression with specific organismal phenotypes.     

ESS gene expression of X-linked imprinted genes subject to sexual selection

We define ESS (Evolutionary Stable Strategy) conditions for the evolution of genomic imprinting at an X-linked locus. The system analysed is designed for mammalian imprinting in which X-linked genes typically undergo random X-inactivation and lack Y-linked homologues. We consider two models that map cellular gene expression to fitness in females subject to random X-inactivation. In the first model, female fitness is simply a function of the average gene expression across all cells. In the second model, each cell contributes independently to fitness, and female fitness is assessed as the average of these contributions across all cells. In both models, imprinting readily evolves when sexual selection favours different levels of gene expression in the two sexes. Imprinting is beneficial as it improves adaptation in both sexes. There are limits to the improvement in adaptation when sexual selection is strong and favours greater gene expression in males (the heterogametic sex). We also consider the consequences of an active Y-linked homologue on the evolution of imprinting. Our analysis suggests that restrictive conditions apply for the evolution of polymorphic ESSs at an X-linked imprinted loci.     

Gene expression and protein length influence codon usage and rates of sequence evolution in Populus tremula

Codon bias is generally thought to be determined by a balance between mutation, genetic drift, and natural selection on translational efficiency. However, natural selection on codon usage is considered to be a weak evolutionary force and selection on codon usage is expected to be strongest in species with large effective population sizes. In this paper, I study associations between codon usage, gene expression, and molecular evolution at synonymous and nonsynonymous sites in the long-lived, woody perennial plant Populus tremula (Salicaceae). Using expression data for 558 genes derived from expressed sequence tags (EST) libraries from 19 different tissues and developmental stages, I study how gene expression levels within single tissues as well as across tissues affect codon usage and rates sequence evolution at synonymous and nonsynonymous sites. I show that gene expression have direct effects on both codon usage and the level of selective constraint of proteins in P. tremula, although in different ways. Codon usage genes is primarily determined by how highly expressed a genes is, whereas rates of sequence evolution are primarily determined by how widely expressed genes are. In addition to the effects of gene expression, protein length appear to be an important factor influencing virtually all aspects of molecular evolution in P. tremula.     

Rapid,broad‐scale gene expression evolution in experimentally harvested fish populations

Gene expression changes potentially play an important role in adaptive evolution under human‐induced selection pressures, but this has been challenging to demonstrate in natural populations. Fishing exhibits strong selection pressure against large body size, thus potentially inducing evolutionary changes in life history and other traits that may be slowly reversible once fishing ceases. However, there is a lack of convincing examples regarding the speed and magnitude of fisheries‐induced evolution, and thus, the relevant underlying molecular‐level effects remain elusive. We use wild‐origin zebrafish (Danio rerio) as a model for harvest‐induced evolution. We experimentally demonstrate broad‐scale gene expression changes induced by just five generations of size‐selective harvesting, and limited genetic convergence following the cessation of harvesting. We also demonstrate significant allele frequency changes in genes that were differentially expressed after five generations of size‐selective harvesting. We further show that nine generations of captive breeding induced substantial gene expression changes in control stocks likely due to inadvertent selection in the captive environment. The large extent and rapid pace of the gene expression changes caused by both harvest‐induced selection and captive breeding emphasizes the need for evolutionary enlightened management towards sustainable fisheries.     

Bistability in feedback circuits as a byproduct of evolution of evolvability

Noisy bistable dynamics in gene regulation can underlie stochastic switching and is demonstrated to be beneficial under fluctuating environments. It is not known, however, if fluctuating selection alone can result in bistable dynamics. Using a stochastic model of simple feedback networks, we apply fluctuating selection on gene expression and run in silico evolutionary simulations. We find that independent of the specific nature of the environment–fitness relationship, the main outcome of fluctuating selection is the evolution of increased evolvability in the network; system parameters evolve toward a nonlinear regime where phenotypic diversity is increased and small changes in genotype cause large changes in expression level. In the presence of noise, the evolution of increased nonlinearity results in the emergence and maintenance of bistability. Our results provide the first direct evidence that bistability and stochastic switching in a gene regulatory network can emerge as a mechanism to cope with fluctuating environments. They strongly suggest that such emergence occurs as a byproduct of evolution of evolvability and exploitation of noise by evolution.     

Directed evolution and the creation of enantioselective biocatalysts

Directed evolution has emerged as a key technology to generate enzymes with new or improved properties that are of major importance to the biotechnology industry. A directed evolution approach starts with the identification of a target enzyme to be optimized and the cloning of the corresponding gene. An efficient expression system is needed before the target gene is subjected to random mutagenesis and/or in vitro recombination, thereby creating molecular diversity. Subsequently, improved enzyme variants are identified, preferably after being secreted into culture medium, by screening or selection for the desired property. The genes encoding the improved enzymes are then used to parent the next round of directed evolution. Enantioselectivity is a biocatalyst property of major biotechnological importance that is, however, difficult to deal with. We discuss recent examples of creating enantioselective biocatalysts by directed evolution.     

Directed indices for exploring gene expression data

MOTIVATION: Large expression studies with clinical outcome data are becoming available for analysis. An important goal is to identify genes or clusters of genes where expression is related to patient outcome. While clustering methods are useful data exploration tools, they do not directly allow one to relate the expression data to clinical outcome. Alternatively, methods which rank genes based on their univariate significance do not incorporate gene function or relationships to genes that have been previously identified. In addition, after sifting through potentially thousands of genes, summary estimates (e.g. regression coefficients or error rates) algorithms should address the potentially large bias introduced by gene selection. RESULTS: We developed a gene index technique that generalizes methods that rank genes by their univariate associations to patient outcome. Genes are ordered based on simultaneously linking their expression both to patient outcome and to a specific gene of interest. The technique can also be used to suggest profiles of gene expression related to patient outcome. A cross-validation method is shown to be important for reducing bias due to adaptive gene selection. The methods are illustrated on a recently collected gene expression data set based on 160 patients with diffuse large cell lymphoma (DLCL).     

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.     

Combining models of protein translation and population genetics to predict protein production rates from codon usage patterns

Genes are often biased in their codon usage. The degree of bias displayed often changes with expression level and intragenic position. Numerous indices, such as the codon adaptation index, have been developed to measure this bias. Although the expression level of a gene and index values are correlated, the heuristic nature of these metrics limits their ability to explain this relationship. As an alternative approach, this study integrates mechanistic models of cellular and population processes in a nested manner to develop a stochastic evolutionary model of a protein's production rate (SEMPPR). SEMPPR assumes that the evolution of codon bias is driven by selection to reduce the cost of nonsense errors and that this selection is counteracted by mutation and drift. Through the application of Bayes' theorem, SEMPPR generates a posterior probability distribution for the protein production rate of a given gene. Conceptually, SEMPPR's predictions are based on the degree of adaptation to reduce the cost of nonsense errors observed in the codon usage pattern of the gene. As an illustration, SEMPPR was parameterized using the Saccharomyces cerevisiae genome and its predictions tested using available empirical data. The results indicate that SEMPPR's predictions are as reliable index based ones. In addition, SEMPPR's output is more easily interpreted and its predictions could be improved through refinements of the models upon which it is built.     

New cancer diagnosis modeling using boosting and projective adaptive resonance theory with improved reliable index

An optimal and individualized treatment protocol based on accurate diagnosis is urgently required for the adequate treatment of patients. For this purpose, it is important to develop a sophisticated algorithm that can manage large amount of data, such as gene expression data from DNA microarray, for optimal and individualized diagnosis. Especially, marker gene selection is essential in the analysis of gene expression data.In the present study, we developed the combination method of projective adaptive resonance theory and boosted fuzzy classifier with SWEEP operator method for model construction and marker selection. And we applied this method to microarray data of acute leukemia and brain tumor. The method enabled the selection of 14 important genes related to the prognosis of the tumor. In addition, we proposed improved reliability index for cancer diagnostic prediction of blinded subjects. Based on the index, the discriminated group with over 90% prediction accuracy was separated from the others.PART-BFCS with improved RI_BFCS method does not only show high performance, but also has the feature of reliable prediction further. This result suggests that PART-BFCS with improved RI_BFCS method has the potential to function as a new method of class prediction for diagnosis of patients.     

Genes under weaker stabilizing selection increase network evolvability and rapid regulatory adaptation to an environmental shift

Regulatory networks play a central role in the modulation of gene expression, the control of cellular differentiation, and the emergence of complex phenotypes. Regulatory networks could constrain or facilitate evolutionary adaptation in gene expression levels. Here, we model the adaptation of regulatory networks and gene expression levels to a shift in the environment that alters the optimal expression level of a single gene. Our analyses show signatures of natural selection on regulatory networks that both constrain and facilitate rapid evolution of gene expression level towards new optima. The analyses are interpreted from the standpoint of neutral expectations and illustrate the challenge to making inferences about network adaptation. Furthermore, we examine the consequence of variable stabilizing selection across genes on the strength and direction of interactions in regulatory networks and in their subsequent adaptation. We observe that directional selection on a highly constrained gene previously under strong stabilizing selection was more efficient when the gene was embedded within a network of partners under relaxed stabilizing selection pressure. The observation leads to the expectation that evolutionarily resilient regulatory networks will contain optimal ratios of genes whose expression is under weak and strong stabilizing selection. Altogether, our results suggest that the variable strengths of stabilizing selection across genes within regulatory networks might itself contribute to the long‐term adaptation of complex phenotypes.