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.     

Protein complexity, gene duplicability and gene dispensability in the yeast genome

Using functional genomic and protein structural data we studied the effects of protein complexity (here defined as the number of subunit types in a protein) on gene dispensability and gene duplicability. We found that in terms of gene duplicability the major distinction in protein complexity is between hetero-complexes, each of which includes at least two different types of subunits (polypeptides), and homo-complexes, which include monomers and complexes that consist of only subunits of one polypeptide type. However, gene dispensability decreases only gradually as the number of subunit types in a protein complex increases. These observations suggest that the dosage balance hypothesis can explain well gene duplicability of complex proteins, but cannot completely explain the difference in dispensabilities between hetero-complex subunits. It is likely that knocking out a gene coding for a hetero-complex subunit would disrupt the function of the whole complex, so that the deletion effect on fitness would increase with protein complexity. We also found that multi-domain polypeptide genes are less dispensable but more duplicable than single-domain polypeptide genes. Duplicate genes derived from the whole genome duplication event in yeast are more dispensable (except for ribosomal protein genes) than other duplicate genes. Further, we found that subunits of the same protein complex tend to have similar expression levels and similar effects of gene deletion on fitness. Finally, we estimated that in yeast the contribution of duplicate genes to genetic robustness against null mutation is approximately 9%, smaller than previously estimated. In yeast, protein complexity may serve as a better indicator of gene dispensability than do duplicate genes.     

Protein evolution is faster outside the cell

Some proteins are highly conserved across all species, whereas others diverge significantly even between closely related species. Attempts have been made to correlate the rate of protein evolution to amino acid composition, protein dispensability, and the number of protein-protein interactions, but in all cases, conflicting studies have shown that the theories are hard to confirm experimentally. The only correlation that is undisputed so far is that highly/broadly expressed proteins seem to evolve at a lower rate. Consequently, it has been suggested that correlations between evolution rate and factors like protein dispensability or the number of protein-protein interactions could be just secondary effects due to differences in expression. The purpose of this study was to analyze mammalian proteins/genes with known subcellular location for variations in evolution rates. We show that proteins that are exported (extracellular proteins) evolve faster than proteins that reside inside the cell (intracellular proteins). We find weak, but significant, correlations between evolution rates and expression levels, percentage of tissues in which the proteins are expressed (expression broadness), and the number of protein interaction partners. More important, we show that the observed difference in evolution rate between extra- and intracellular proteins is largely independent of expression levels, expression broadness, and the number of protein-protein interactions. We also find that the difference is not caused by an overrepresentation of immunological proteins or disulfide bridge-containing proteins among the extracellular data set. We conclude that the subcellular location of a mammalian protein has a larger effect on its evolution rate than any of the other factors studied in this paper, including expression levels/patterns. We observe a difference in evolution rates between extracellular and intracellular proteins for a yeast data set as well and again show that it is completely independent of expression levels.     

Assessing the determinants of evolutionary rates in the presence of noise

Although protein sequences are known to evolve at vastly different rates, little is known about what determines their rate of evolution. However, a recent study using principal component regression (PCR) has concluded that evolutionary rates in yeast are primarily governed by a single determinant related to translation frequency. Here, we demonstrate that noise in biological data can confound PCRs, leading to spurious conclusions. When equalizing noise levels across 7 predictor variables used in previous studies, we find no evidence that protein evolution is dominated by a single determinant. Our results indicate that a variety of factors--including expression level, gene dispensability, and protein-protein interactions--may independently affect evolutionary rates in yeast. More accurate measurements or more sophisticated statistical techniques will be required to determine which one, if any, of these factors dominates protein evolution.     

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.     

Rate of protein evolution versus fitness effect of gene deletion

Whether nonessential genes evolve faster than essential genes has been a controversial issue. To resolve this issue, we use the data from a nearly complete set of single-gene deletions in the yeast Saccharomyces cerevisiae to assess protein dispensability. Also, instead of the nematode, which was used previously but is only distantly related to S. cerevisiae, we use another yeast, Candida albicans, as a second species to estimate the evolutionary distances between orthologous genes in two species. Our analysis reveals only a weak correlation between protein dispensability and evolutionary rate. More important, the correlation disappears when duplicate genes are removed from the analysis. And surprisingly, the average rate of nonsynonymous substitution is considerably lower than that for single-copy genes in the yeast genome. This observation suggests that structural constraints are more important in determining the rate of evolution of a protein than dispensability because duplicate genes are on average more dispensable than single-copy genes. For duplicate genes, those with only a weak effect or no effect of deletion on fitness evolve on average faster than those with a moderate or strong effect of deletion on fitness, which in turn evolve on average faster than those with a lethal effect of deletion.     

Functional analysis of gene duplications in Saccharomyces cerevisiae

Gene duplication can occur on two scales: whole-genome duplications (WGD) and smaller-scale duplications (SSD) involving individual genes or genomic segments. Duplication may result in functionally redundant genes or diverge in function through neofunctionalization or subfunctionalization. The effect of duplication scale on functional evolution has not yet been explored, probably due to the lack of global knowledge of protein function and different times of duplication events. To address this question, we used integrated Bayesian analysis of diverse functional genomic data to accurately evaluate the extent of functional similarity and divergence between paralogs on a global scale. We found that paralogs resulting from the whole-genome duplication are more likely to share interaction partners and biological functions than smaller-scale duplicates, independent of sequence similarity. In addition, WGD paralogs show lower frequency of essential genes and higher synthetic lethality rate, but instead diverge more in expression pattern and upstream regulatory region. Thus, our analysis demonstrates that WGD paralogs generally have similar compensatory functions but diverging expression patterns, suggesting a potential of distinct evolutionary scenarios for paralogs that arose through different duplication mechanisms. Furthermore, by identifying these functional disparities between the two types of duplicates, we reconcile previous disputes on the relationship between sequence divergence and expression divergence or essentiality.     

Understanding relationship between sequence and functional evolution in yeast proteins

The underlying relationship between functional variables and sequence evolutionary rates is often assessed by partial correlation analysis. However, this strategy is impeded by the difficulty of conducting meaningful statistical analysis using noisy biological data. A recent study suggested that the partial correlation analysis is misleading when data is noisy and that the principal component regression analysis is a better tool to analyze biological data. In this paper, we evaluate how these two statistical tools (partial correlation and principal component regression) perform when data are noisy. Contrary to the earlier conclusion, we found that these two tools perform comparably in most cases. Furthermore, when there is more than one ‘true’ independent variable, partial correlation analysis delivers a better representation of the data. Employing both tools may provide a more complete and complementary representation of the real data. In this light, and with new analyses, we suggest that protein length and gene dispensability play significant, independent roles in yeast protein evolution. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

植物miRNA的进化

鉴于miRNA在植物基因表达调控中的重要作用, 人们已经开展对植物miRNA的预测、鉴定、功能和进化等方面的研究。随着许多模式植物基因组测序的完成, miRNA的基因组学和进化信息的整合为miRNA的起源和进化研究提供了越来越多的证据和假说, 然而尚未见关于植物miRNA进化方面的系统报道。文章从miRNA的起源以及相应的几种假说、miRNA的产生和消亡、miRNA的功能进化等几方面来分析和综述植物miRNA进化的研究进展。     

Gene complexity and gene duplicability

Eukaryotic genes are on average more complex than prokaryotic genes in terms of expression regulation, protein length, and protein-domain structure [1-5]. Eukaryotes are also known to have a higher rate of gene duplication than prokaryotes do [6, 7]. Because gene duplication is the primary source of new genes [], the average gene complexity in a genome may have been increased by gene duplication if complex genes are preferentially duplicated. Here, we test this "gene complexity and gene duplicability" hypothesis with yeast genomic data. We show that, on average, duplicate genes from either whole-genome or individual-gene duplication have longer protein sequences, more functional domains, and more cis-regulatory motifs than singleton genes. This phenomenon is not a by-product of previously known mechanisms, such as protein function [10-13], evolutionary rate [14, 15], dosage [11], and dosage balance [16], that influence gene duplicability. Rather, it appears to have resulted from the sub-neo-functionalization process in duplicate-gene evolution [11]. Under this process, complex genes are more likely to be retained after duplication because they are prone to subfunctionalization, and gene complexity is regained via subsequent neofunctionalization. Thus, gene duplication increases both gene number and gene complexity, two important factors in the origin of genomic and organismal complexity.     

Evolutionary systems biology: links between gene evolution and function

The recent accumulation of genome-wide data on various facets of gene expression, function and evolution stimulated the emergence of a new field, evolutionary systems biology. Many significant correlations were detected between variables that characterize the functioning of a gene, such as expression level, knockout effect, connectivity of genetic and protein-protein interaction networks, and variables that describe gene evolution, such as sequence evolution rate and propensity for gene loss. The first attempts on multidimensional analysis of genomic data yielded composite variables that describe the 'status' of a gene in the genomic community. However, it remains uncertain whether different functional variables affect gene evolution synergistically or there is a single, dominant factor. The number of translation events, linked to selection for translational robustness, was proposed as a candidate for such a major determinant of protein evolution. These developments show that, although the methodological basis of evolutionary systems biology is not yet fully solidified, this area of research is already starting to yield fundamental biological insights.     

An integrated view of the correlations between genomic and phenomic variables

Genome sequencing opened the flood gate of ＂-omics＂ studies, among which the research about correlations between genomic and phenomic variables is an important part. With the development of functional genomics and systems biology, genome-wide investigation of the correlations between many genomic and phenomic variables became possible. In this review, five genomic variables, such as evolution rate （or ＂age＂ of the gene）, the length of intron and ORF （protein length） in one gene, the biases of amino acid composition and codon usage, along with the phenomic variables related to expression patterns （level and breadth） are focused on. In most cases, genes with higher mRNA/protein expression level tend to evolve slowly, have less intronic DNA, code for smaller proteins, and have higher biases of amino acid composition and codon usage. In addition, broadly expressed proteins evolve more slowly and are shorter than tissue-specific proteins. Studies in this field are helpful for deeper understanding the signatures of selection mediated by the features of gene expression and are of great significance to enrich the evolution theory.     

An integrated view of protein evolution

Why do proteins evolve at different rates? Advances in systems biology and genomics have facilitated a move from studying individual proteins to characterizing global cellular factors. Systematic surveys indicate that protein evolution is not determined exclusively by selection on protein structure and function, but is also affected by the genomic position of the encoding genes, their expression patterns, their position in biological networks and possibly their robustness to mistranslation. Recent work has allowed insights into the relative importance of these factors. We discuss the status of a much-needed coherent view that integrates studies on protein evolution with biochemistry and functional and structural genomics.     

Evolution of proteins and gene expression levels are coupled in Drosophila and are independently associated with mRNA abundance, protein length, and number of protein-protein interactions

Organismic evolution requires that variation at distinct hierarchical levels and attributes be coherently integrated, often in the face of disparate environmental and genetic pressures. A central part of the evolutionary analysis of biological systems remains to decipher the causal connections between organism-wide (or genome-wide) attributes (e.g., mRNA abundance, protein length, codon bias, recombination rate, genomic position, mutation rate, etc) as well as their role-together with mutation, selection, and genetic drift-in shaping patterns of evolutionary variation in any of the attributes themselves. Here we combine genome-wide evolutionary analysis of protein and gene expression data to highlight fundamental relationships among genomic attributes and their associations with the evolution of both protein sequences and gene expression levels. Our results show that protein divergence is positively coupled with both gene expression polymorphism and divergence. We show moreover that although the number of protein-protein interactions in Drosophila is negatively associated with protein divergence as well as gene expression polymorphism and divergence, protein-protein interactions cannot account for the observed coupling between regulatory and structural evolution. Furthermore, we show that proteins with higher rates of amino acid substitutions tend to have larger sizes and tend to be expressed at lower mRNA abundances, whereas genes with higher levels of gene expression divergence and polymorphism tend to have shorter sizes and tend to be expressed at higher mRNA abundances. Finally, we show that protein length is negatively associated with both number of protein-protein interactions and mRNA abundance and that interacting proteins in Drosophila show similar amounts of divergence. We suggest that protein sequences and gene expression are subjected to similar evolutionary dynamics, possibly because of similarity in the fitness effect (i.e., strength of stabilizing selection) of disruptions in a gene's protein sequence or its mRNA expression. We conclude that, as more and better data accumulate, understanding the causal connections among biological traits and how they are integrated over time to constrain or promote structural and regulatory evolution may finally become possible.     

Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae

As we are moving into the post genome-sequencing era, various high-throughput experimental techniques have been developed to characterize biological systems on the genomic scale. Discovering new biological knowledge from the high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a Bayesian statistical method together with Boltzmann machine and simulated annealing for protein functional annotation in the yeast Saccharomyces cerevisiae through integrating various high-throughput biological data, including yeast two-hybrid data, protein complexes and microarray gene expression profiles. In our approach, we quantified the relationship between functional similarity and high-throughput data, and coded the relationship into ‘functional linkage graph’, where each node represents one protein and the weight of each edge is characterized by the Bayesian probability of function similarity between two proteins. We also integrated the evolution information and protein subcellular localization information into the prediction. Based on our method, 1802 out of 2280 unannotated proteins in yeast were assigned functions systematically.     

Mining protein networks for synthetic genetic interactions

Background  

The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified.