Functional genomic analysis of the rates of 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.

Why highly expressed proteins evolve slowly

Much recent work has explored molecular and population-genetic constraints on the rate of protein sequence evolution. The best predictor of evolutionary rate is expression level, for reasons that have remained unexplained. Here, we hypothesize that selection to reduce the burden of protein misfolding will favor protein sequences with increased robustness to translational missense errors. Pressure for translational robustness increases with expression level and constrains sequence evolution. Using several sequenced yeast genomes, global expression and protein abundance data, and sets of paralogs traceable to an ancient whole-genome duplication in yeast, we rule out several confounding effects and show that expression level explains roughly half the variation in Saccharomyces cerevisiae protein evolutionary rates. We examine causes for expression's dominant role and find that genome-wide tests favor the translational robustness explanation over existing hypotheses that invoke constraints on function or translational efficiency. Our results suggest that proteins evolve at rates largely unrelated to their functions and can explain why highly expressed proteins evolve slowly across the tree of life.

Higher duplicability of less important genes in yeast genomes

Gene duplication plays an important role in evolution because it is the primary source of new genes. Many recent studies showed that gene duplicability varies considerably among genes. Several considerations led us to hypothesize that less important genes have higher rates of successful duplications, where gene importance is measured by the fitness reduction caused by the deletion of the gene. Here, we test this hypothesis by comparing the importance of two groups of singleton genes in the yeast Saccharomyces cerevisiae (Sce). Group S genes did not duplicate in four other yeast species examined, whereas group D experienced duplication in these species. Consistent with our hypothesis, we found group D genes to be less important than group S genes. Specifically, 17% of group D genes are essential in Sce, compared to 28% for group S. Furthermore, deleting a group D gene in Sce reduces the fitness by 24% on average, compared to 38% for group S. Our subsequent analysis showed that less important genes have more cis-regulatory motifs, which could lead to a higher chance of subfunctionalization of duplicate genes and result in an enhanced rate of gene retention. Less important genes may also have weaker dosage imbalance effects and cause fewer genetic perturbations when duplicated. Regardless of the cause, our observation indicates that the previous finding of a less severe fitness consequence of deleting a duplicate gene than deleting a singleton gene is at least in part due to the fact that duplicate genes are intrinsically less important than singleton genes and suggests that the contribution of duplicate genes to genetic robustness has been overestimated.

Using Gene-History and Expression Analyses to Assess the Involvement of LGI Genes in Human Disorders

Mutations in the leucine-rich, glioma-inactivated 1 gene, LGI1, cause autosomal-dominant lateral temporal lobe epilepsy via unknown mechanisms. LGI1 belongs to a subfamily of leucine-rich repeat genes comprising four members (LGI1-LGI4) in mammals. In this study, both comparative developmental as well as molecular evolutionary methods were applied to investigate the evolution of the LGI gene family and, subsequently, of the functional importance of its different gene members. Our phylogenetic studies suggest that LGI genes evolved early in the vertebrate lineage. Genetic and expression analyses of all five zebrafish lgi genes revealed duplications of lgi1 and lgi2, each resulting in two paralogous gene copies with mostly nonoverlapping expression patterns. Furthermore, all vertebrate LGI1 orthologs experience high levels of purifying selection that argue for an essential role of this gene in neural development or function. The approach of combining expression and selection data used here exemplarily demonstrates that in poorly characterized gene families a framework of evolutionary and expression analyses can identify those genes that are functionally most important and are therefore prime candidates for human disorders.

The application of statistical physics to evolutionary biology

A number of fundamental mathematical models of the evolutionary process exhibit dynamics that can be difficult to understand analytically. Here we show that a precise mathematical analogy can be drawn between certain evolutionary and thermodynamic systems, allowing application of the powerful machinery of statistical physics to analysis of a family of evolutionary models. Analytical results that follow directly from this approach include the steady-state distribution of fixed genotypes and the load in finite populations. The analogy with statistical physics also reveals that, contrary to a basic tenet of the nearly neutral theory of molecular evolution, the frequencies of adaptive and deleterious substitutions at steady state are equal. Finally, just as the free energy function quantitatively characterizes the balance between energy and entropy, a free fitness function provides an analytical expression for the balance between natural selection and stochastic drift.