Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

Speciation and development

Understanding general principles about the origin of species remains one of the foundational challenges in evolutionary biology. The genomic divergence between groups of individuals can spawn hybrid inviability and hybrid sterility, which presents a tantalizing developmental problem. Divergent developmental programs may yield either conserved or divergent phenotypes relative to ancestral traits, both of which can be responsible for reproductive isolation during the speciation process. The genetic mechanisms of developmental evolution involve cis- and trans-acting gene regulatory change, protein-protein interactions, genetic network structures, dosage, and epigenetic regulation, all of which also have roots in population genetic and molecular evolutionary processes. Toward the goal of demystifying Darwin's "mystery of mysteries," this review integrates microevolutionary concepts of genetic change with principles of organismal development, establishing explicit links between population genetic process and developmental mechanisms in the production of macroevolutionary pattern. This integration aims to establish a more unified view of speciation that binds process and mechanism.

Chaperone client proteins evolve slower than non-client proteins

Chaperones are important molecular machinery that assists proteins to attain their native three-dimensional structure crucial for function. Earlier studies using experimental evolution showed that chaperones impose a relaxation of sequence constraints on their "client" proteins, which may lead to the fixation of slightly deleterious mutations on the latter. However, we hypothesized that such a phenomenon might be harmful to the organism in a natural physiological condition. In this study, we investigated the evolutionary rates of chaperone client and non-client proteins in five model organisms from both prokaryotic and eukaryotic lineages. Our study reveals a slower evolutionary rate of chaperone client proteins in all five organisms. Additionally, the slower folding rate and lower aggregation propensity of chaperone client proteins reveal that the chaperone may play an essential role in rescuing the slightly disadvantageous effects due to random mutations and subsequent protein misfolding. However, the fixation of such mutations is less likely to be selected in the natural population.

Gene connectivity and enzyme evolution in the human metabolic network

BACKGROUND

Determining the factors involved in the likelihood of a gene being under adaptive selection is still a challenging goal in Evolutionary Biology. Here, we perform an evolutionary analysis of the human metabolic genes to explore the associations between network structure and the presence and strength of natural selection in the genes whose products are involved in metabolism. Purifying and positive selection are estimated at interspecific (among mammals) and intraspecific (among human populations) levels, and the connections between enzymatic reactions are differentiated between incoming (in-degree) and outgoing (out-degree) links.

RESULTS

We confirm that purifying selection has been stronger in highly connected genes. Long-term positive selection has targeted poorly connected enzymes, whereas short-term positive selection has targeted different enzymes depending on whether the selective sweep has reached fixation in the population: genes under a complete selective sweep are poorly connected, whereas those under an incomplete selective sweep have high out-degree connectivity. The last steps of pathways are more conserved due to stronger purifying selection, with long-term positive selection targeting preferentially enzymes that catalyze the first steps. However, short-term positive selection has targeted enzymes that catalyze the last steps in the metabolic network. Strong signals of positive selection have been found for metabolic processes involved in lipid transport and membrane fluidity and permeability.

CONCLUSIONS

Our analysis highlights the importance of analyzing the same biological system at different evolutionary timescales to understand the evolution of metabolic genes and of distinguishing between incoming and outgoing links in a metabolic network. Short-term positive selection has targeted enzymes with a different connectivity profile depending on the completeness of the selective sweep, while long-term positive selection has targeted genes with fewer connections that code for enzymes that catalyze the first steps in the network.

REVIEWERS

This article was reviewed by Diamantis Sellis and Brandon Invergo.

Codon Usage Differences among Genes Expressed in Different Tissues of Drosophila melanogaster

Codon usage patterns are affected by both mutational biases and translational selection. The frequency at which each codon is used in the genome is directly linked to the cellular concentrations of their corresponding tRNAs. Transfer RNA abundances—as well as the abundances of other potentially relevant factors, such as RNA-binding proteins—may vary across different tissues, making it possible that genes expressed in different tissues are subject to different translational selection regimes, and thus differ in their patterns of codon usage. These differences, however, are poorly understood, having been studied only in Arabidopsis, rice and human, with controversial results in human. Drosophila melanogaster is a suitable model organism to study tissue-specific codon adaptation given its large effective population size. Here, we compare 2,046 genes, each expressed specifically in one tissue of D. melanogaster. We show that genes expressed in different tissues exhibit significant differences in their patterns of codon usage, and that these differences are only partially due to differences in GC content, expression levels, or protein lengths. Remarkably, these differences are stronger when analyses are restricted to highly expressed genes. Our results strongly suggest that genes expressed in different tissues are subject to different regimes of translational selection.

What lies beneath? Molecular evolution during the radiation of caecilian amphibians

Background

Evolution leaves an imprint in species through genetic change. At the molecular level, evolutionary changes can be explored by studying ratios of nucleotide substitutions. The interplay among molecular evolution, derived phenotypes, and ecological ranges can provide insights into adaptive radiations. Caecilians (order Gymnophiona), probably the least known of the major lineages of vertebrates, are limbless tropical amphibians, with adults of most species burrowing in soils (fossoriality). This enigmatic order of amphibians are very distinct phenotypically from other extant amphibians and likely from the ancestor of Lissamphibia, but little to nothing is known about the molecular changes underpinning their radiation. We hypothesised that colonization of various depths of tropical soils and of freshwater habitats presented new ecological opportunities to caecilians.

Results

A total of 8540 candidate groups of orthologous genes from transcriptomic data of five species of caecilian amphibians and the genome of the frog Xenopus tropicalis were analysed in order to investigate the genetic machinery behind caecilian diversification. We found a total of 168 protein-coding genes with signatures of positive selection at different evolutionary times during the radiation of caecilians. The majority of these genes were related to functional elements of the cell membrane and extracellular matrix with expression in several different tissues. The first colonization of the tropical soils was connected to the largest number of protein-coding genes under positive selection in our analysis. From the results of our study, we highlighted molecular changes in genes involved in perception, reduction-oxidation processes, and aging that likely were involved in the adaptation to different soil strata.

Conclusions

The genes inferred to have been under positive selection provide valuable insights into caecilian evolution, potentially underpin adaptations of caecilians to their extreme environments, and contribute to a better understanding of fossorial adaptations and molecular evolution in vertebrates.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5694-1) contains supplementary material, which is available to authorized users.

N-glycoproteins exhibit a positive expression level-evolutionary rate correlation

The different proteins of any proteome evolve at enormously different rates. One of the primary factors influencing rates of protein evolution is expression level, with highly expressed proteins tending to evolve at slow rates. This phenomenon, known as the expression level-evolutionary rate (E-R) anticorrelation, has been attributed to the abundance-dependent deleterious effects of misfolding or misinteraction. We have recently shown that secreted proteins either lack an E-R anticorrelation or exhibit a significantly reduced E-R anticorrelation. This effect may be due to the strict quality control to which secreted proteins are subject in the endoplasmic reticulum (which is expected to reduce the rate of misfolding and its deleterious effects) or to their extracellular location (expected to reduce the rate of misinteraction and its deleterious effects). Among secreted proteins, N-glycosylated ones are under particularly strong quality control. Here, we investigate how N-linked glycosylation affects the E-R anticorrelation. Strikingly, we observe a positive E-R correlation among N-glycosylated proteins. That is, N-glycoproteins that are highly expressed evolve at faster rates than lowly expressed N-glycoproteins, in contrast to what is observed among intracellular proteins.

Recording negative results of protein-protein interaction assays: an easy way to deal with the biases and errors of interactomic data sets

In recent years, it has become increasingly common to use assays that can test whether two proteins interact, such as yeast two-hybrid and tandem affinity purification followed by mass spectrometry. Such techniques, particularly when applied at a large scale, suffer from high rates of false positives and false negatives. In addition, interactomic data sets are subjected to a number of biases, which limits considerably their usefulness to address biological questions. Interactomic databases only keep track of the positive results of protein interaction assays (those indicating that the tested proteins interact). Despite their importance, negative results (those indicating that the tested proteins do not interact) are not recorded in interactomic databases. Indeed, current interactomic databases do not support negative results. Here, I argue that systematically recording not only positive but also negative results of protein interaction assays would help scientists identify errors and deal with biases, thus enormously increasing the value of interactomic data sets. The challenges of implementing this change, along with potential solutions, are discussed.

Protein intrinsic disorder negatively associates with gene age in different eukaryotic lineages

The emergence of new protein-coding genes in a specific lineage or species provides raw materials for evolutionary adaptations. Until recently, the biology of new genes emerging particularly from non-genic sequences remained unexplored. Although the new genes are subjected to variable selection pressure and face rapid deletion, some of them become functional and are retained in the gene pool. To acquire functional novelties, new genes often get integrated into the pre-existing ancestral networks. However, the mechanism by which young proteins acquire novel interactions remains unanswered till date. Since structural orientation contributes hugely to the mode of proteins' physical interactions, in this regard, we put forward an interesting question - Do new genes encode proteins with stable folds? Addressing the question, we demonstrated that the intrinsic disorder inversely correlates with the evolutionary gene ages - i.e. young proteins are richer in intrinsic disorder than the ancient ones. We further noted that young proteins, which are initially poorly connected hubs, prefer to be structurally more disordered than well-connected ancient proteins. The phenomenon strikingly defies the usual trend of well-connected proteins being highly disordered in structure. We justified that structural disorder might help poorly connected young proteins to undergo promiscuous interactions, which provides the foundation for novel protein interactions. The study focuses on the evolutionary perspectives of young proteins in the light of structural adaptations.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

Detecting gene subnetworks under selection in biological pathways

Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude.

Intrinsic incompatibilities evolving as a by-product of divergent ecological selection: Considering them in empirical studies on divergence with gene flow

The possibility of intrinsic barriers to gene flow is often neglected in empirical research on local adaptation and speciation with gene flow, for example when interpreting patterns observed in genome scans. However, we draw attention to the fact that, even with gene flow, divergent ecological selection may generate intrinsic barriers involving both ecologically selected and other interacting loci. Mechanistically, the link between the two types of barriers may be generated by genes that have multiple functions (i.e., pleiotropy), and/or by gene interaction networks. Because most genes function in complex networks, and their evolution is not independent of other genes, changes evolving in response to ecological selection can generate intrinsic barriers as a by-product. A crucial question is to what extent such by-product barriers contribute to divergence and speciation-that is whether they stably reduce gene flow. We discuss under which conditions by-product barriers may increase isolation. However, we also highlight that, depending on the conditions (e.g., the amount of gene flow and the strength of selection acting on the intrinsic vs. the ecological barrier component), the intrinsic incompatibility may actually destabilize barriers to gene flow. In practice, intrinsic barriers generated as a by-product of divergent ecological selection may generate peaks in genome scans that cannot easily be interpreted. We argue that empirical studies on divergence with gene flow should consider the possibility of both ecological and intrinsic barriers. Future progress will likely come from work combining population genomic studies, experiments quantifying fitness and molecular studies on protein function and interactions.