Recent positive selection has acted on genes encoding proteins with more interactions within the whole human interactome

An efficient and robust ABC approach to infer the rate and strength of adaptation

Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in nonmodel species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to nonmodel genomes. We apply ABC-MK to the human proteome and a set of known virus interacting proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.

Assessing the Presence of Recent Adaptation in the Human Genome With Mixture Density Regression

How much genome differences between species reflect neutral or adaptive evolution is a central question in evolutionary genomics. In humans and other mammals, the presence of adaptive versus neutral genomic evolution has proven particularly difficult to quantify. The difficulty notably stems from the highly heterogeneous organization of mammalian genomes at multiple levels (functional sequence density, recombination, etc.) which complicates the interpretation and distinction of adaptive versus neutral evolution signals. In this study, we introduce mixture density regressions (MDRs) for the study of the determinants of recent adaptation in the human genome. MDRs provide a flexible regression model based on multiple Gaussian distributions. We use MDRs to model the association between recent selection signals and multiple genomic factors likely to affect the occurrence/detection of positive selection, if the latter was present in the first place to generate these associations. We find that an MDR model with two Gaussian distributions provides an excellent fit to the genome-wide distribution of a common sweep summary statistic (integrated haplotype score), with one of the two distributions likely enriched in positive selection. We further find several factors associated with signals of recent adaptation, including the recombination rate, the density of regulatory elements in immune cells, GC content, gene expression in immune cells, the density of mammal-wide conserved elements, and the distance to the nearest virus-interacting gene. These results support the presence of strong positive selection in recent human evolution and highlight MDRs as a powerful tool to make sense of signals of recent genomic adaptation.

Whole-Genome Sequencing Reveals That Regulatory and Low Pleiotropy Variants Underlie Local Adaptation to Environmental Variability in Purple Sea Urchins

AbstractOrganisms experience environments that vary across both space and time. Such environmental heterogeneity shapes standing genetic variation and may influence species' capacity to adapt to rapid environmental change. However, we know little about the kind of genetic variation that is involved in local adaptation to environmental variability. To address this gap, we sequenced the whole genomes of 140 purple sea urchins (Strongylocentrotus purpuratus) from seven populations that vary in their degree of pH variability. Despite no evidence of global population structure, we found a suite of single-nucleotide polymorphisms (SNPs) tightly correlated with local pH variability (outlier SNPs), which were overrepresented in regions putatively involved in gene regulation (long noncoding RNA and enhancers), supporting the idea that variation in regulatory regions is important for local adaptation to variability. In addition, outliers in genes were found to be (i) enriched for biomineralization and ion homeostasis functions related to low pH response, (ii) less central to the protein-protein interaction network, and (iii) underrepresented among genes highly expressed during early development. Taken together, these results suggest that loci that underlie local adaptation to pH variability in purple sea urchins fall in regions with potentially low pleiotropic effects (based on analyses involving regulatory regions, network centrality, and expression time) involved in low pH response (based on functional enrichment).

An efficient and robust ABC approach to infer the rate and strength of adaptation

Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in non-model species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to non-model genomes. We apply ABC-MK to the human proteome and a set of known Virus Interacting Proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.

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.

Ortholog genes from cactophilic Drosophila provide insight into human adaptation to hallucinogenic cacti

Cultural transformations of lifestyles and dietary practices have been key drivers of human evolution. However, while most of the evidence of genomic adaptations is related to the hunter-gatherer transition to agricultural societies, little is known on the influence of other major cultural manifestations. Shamanism is considered the oldest religion that predominated throughout most of human prehistory and still prevails in many indigenous populations. Several lines of evidence from ethno-archeological studies have demonstrated the continuity and importance of psychoactive plants in South American cultures. However, despite the well-known importance of secondary metabolites in human health, little is known about its role in the evolution of ethnic differences. Herein, we identified candidate genes of adaptation to hallucinogenic cactus in Native Andean populations with a long history of shamanic practices. We used genome-wide expression data from the cactophilic fly Drosophila buzzatii exposed to a hallucinogenic columnar cactus, also consumed by humans, to identify ortholog genes exhibiting adaptive footprints of alkaloid tolerance. Genomic analyses in human populations revealed a suite of ortholog genes evolving under recent positive selection in indigenous populations of the Central Andes. Our results provide evidence of selection in genetic variants related to alkaloids toxicity, xenobiotic metabolism, and neuronal plasticity in Aymara and Quechua populations, suggesting a possible process of gene-culture coevolution driven by religious practices.

Two decades of suspect evidence for adaptive molecular evolution – Negative selection confounding positive selection signals

There has been a large literature in the last two decades affirming adaptive DNA sequence evolution between species. The main lines of evidence are from (i) the McDonald-Kreitman (MK) test, which compares divergence and polymorphism data, and (ii) the phylogenetic analysis by maximum likelihood (PAML) test, which analyzes multispecies divergence data. Here, we apply these two tests concurrently to genomic data of Drosophila and Arabidopsis. To our surprise, the >100 genes identified by the two tests do not overlap beyond random expectation. Because the non-concordance could be due to low powers leading to high false negatives, we merge every 20–30 genes into a ‘supergene’. At the supergene level, the power of detection is large but the calls still do not overlap. We rule out methodological reasons for the non-concordance. In particular, extensive simulations fail to find scenarios whereby positive selection can only be detected by either MK or PAML, but not both. Since molecular evolution is governed by positive and negative selection concurrently, a fundamental assumption for estimating one of these (say, positive selection) is that the other is constant. However, in a broad survey of primates, birds, Drosophila and Arabidopsis, we found that negative selection rarely stays constant for long in evolution. As a consequence, the variation in negative selection is often misconstrued as a signal of positive selection. In conclusion, MK, PAML and any method that examines genomic sequence evolution has to explicitly address the variation in negative selection before estimating positive selection. In a companion study, we propose a possible path forward in two stages—first, by mapping out the changes in negative selection and then using this map to estimate positive selection. For now, the large literature on positive selection between species has to await reassessment.

Decreased recent adaptation at human mendelian disease genes as a possible consequence of interference between advantageous and deleterious variants

Advances in genome sequencing have improved our understanding of the genetic basis of human diseases, and thousands of human genes have been associated with different diseases. Recent genomic adaptation at disease genes has not been well characterized. Here, we compare the rate of strong recent adaptation in the form of selective sweeps between mendelian, non-infectious disease genes and non-disease genes across distinct human populations from the 1000 Genomes Project. We find that mendelian disease genes have experienced far less selective sweeps compared to non-disease genes especially in Africa. Investigating further the possible causes of the sweep deficit at disease genes, we find that this deficit is very strong at disease genes with both low recombination rates and with high numbers of associated disease variants, but is almost non-existent at disease genes with higher recombination rates or lower numbers of associated disease variants. Because segregating recessive deleterious variants have the ability to interfere with adaptive ones, these observations strongly suggest that adaptation has been slowed down by the presence of interfering recessive deleterious variants at disease genes. These results suggest that disease genes suffer from a transient inability to adapt as fast as the rest of the genome.

Dissecting genomic determinants of positive selection with an evolution-guided regression model

In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald–Kreitman (MK) test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the MK test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independent effect of a genomic feature, holding constant all other genomic features. Using the MK regression, I identify numerous genomic features driving positive selection in chimpanzees. These features include well-known ones, such as local mutation rate, residue exposure level, tissue specificity, and immune genes, as well as new features not previously reported, such as gene expression level and metabolic genes. In particular, I show that highly expressed genes may have a higher adaptation rate than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection. Also, I show that metabolic genes may have a higher adaptation rate than their nonmetabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.

An ancient viral epidemic involving host coronavirus interacting genes more than 20,000 years ago in East Asia

The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the vulnerability of human populations to novel viral pressures, despite the vast array of epidemiological and biomedical tools now available. Notably, modern human genomes contain evolutionary information tracing back tens of thousands of years, which may help identify the viruses that have impacted our ancestors-pointing to which viruses have future pandemic potential. Here, we apply evolutionary analyses to human genomic datasets to recover selection events involving tens of human genes that interact with coronaviruses, including SARS-CoV-2, that likely started more than 20,000 years ago. These adaptive events were limited to the population ancestral to East Asian populations. Multiple lines of functional evidence support an ancient viral selective pressure, and East Asia is the geographical origin of several modern coronavirus epidemics. An arms race with an ancient coronavirus, or with a different virus that happened to use similar interactions as coronaviruses with human hosts, may thus have taken place in ancestral East Asian populations. By learning more about our ancient viral foes, our study highlights the promise of evolutionary information to better predict the pandemics of the future. Importantly, adaptation to ancient viral epidemics in specific human populations does not necessarily imply any difference in genetic susceptibility between different human populations, and the current evidence points toward an overwhelming impact of socioeconomic factors in the case of coronavirus disease 2019 (COVID-19).

Polygenic Adaptation: Integrating Population Genetics and Gene Regulatory Networks

The adaptation of populations to local environments often relies on the selection of optimal values for polygenic traits. Here, we first summarize the results obtained from different quantitative genetics and population genetics models, about the genetic architecture of polygenic traits and their response to directional selection. We then highlight the contribution of systems biology to the understanding of the molecular bases of polygenic traits and the evolution of gene regulatory networks involved in these traits. Finally, we discuss the need for a unifying framework merging the fields of population genetics, quantitative genetics and systems biology to better understand the molecular bases of polygenic traits adaptation.

Robust inference of positive selection on regulatory sequences in the human brain

A longstanding hypothesis is that divergence between humans and chimpanzees might have been driven more by regulatory level adaptations than by protein sequence adaptations. This has especially been suggested for regulatory adaptations in the evolution of the human brain. We present a new method to detect positive selection on transcription factor binding sites on the basis of measuring predicted affinity change with a machine learning model of binding. Unlike other methods, this approach requires neither defining a priori neutral sites nor detecting accelerated evolution, thus removing major sources of bias. We scanned the signals of positive selection for CTCF binding sites in 29 human and 11 mouse tissues or cell types. We found that human brain-related cell types have the highest proportion of positive selection. This result is consistent with the view that adaptive evolution to gene regulation has played an important role in evolution of the human brain.

Ancient RNA virus epidemics through the lens of recent adaptation in human genomes

Over the course of the last several million years of evolution, humans probably have been plagued by hundreds or perhaps thousands of epidemics. Little is known about such ancient epidemics and a deep evolutionary perspective on current pathogenic threats is lacking. The study of past epidemics has typically been limited in temporal scope to recorded history, and in physical scope to pathogens that left sufficient DNA behind, such as Yersinia pestis during the Great Plague. Host genomes, however, offer an indirect way to detect ancient epidemics beyond the current temporal and physical limits. Arms races with pathogens have shaped the genomes of the hosts by driving a large number of adaptations at many genes, and these signals can be used to detect and further characterize ancient epidemics. Here, we detect the genomic footprints left by ancient viral epidemics that took place in the past approximately 50 000 years in the 26 human populations represented in the 1000 Genomes Project. By using the enrichment in signals of adaptation at approximately 4500 host loci that interact with specific types of viruses, we provide evidence that RNA viruses have driven a particularly large number of adaptive events across diverse human populations. These results suggest that different types of viruses may have exerted different selective pressures during human evolution. Knowledge of these past selective pressures will provide a deeper evolutionary perspective on current pathogenic threats.

This article is part of the theme issue ‘Insights into health and disease from ancient biomolecules’.

Molecular Characterization, Protein-Protein Interaction Network, and Evolution of Four Glutathione Peroxidases from Tetrahymena thermophila

Glutathione peroxidases (GPxs) form a broad family of antioxidant proteins essential for maintaining redox homeostasis in eukaryotic cells. In this study, we used an integrative approach that combines bioinformatics, molecular biology, and biochemistry to investigate the role of GPxs in reactive oxygen species detoxification in the unicellular eukaryotic model organism Tetrahymena thermophila. Both phylogenetic and mechanistic empirical model analyses provided indications about the evolutionary relationships among the GPXs of Tetrahymena and the orthologous enzymes of phylogenetically related species. In-silico gene characterization and text mining were used to predict the functional relationships between GPxs and other physiologically-relevant processes. The GPx genes contain conserved transcriptional regulatory elements in the promoter region, which suggest that transcription is under tight control of specialized signaling pathways. The bioinformatic findings were next experimentally validated by studying the time course of gene transcription and enzymatic activity after copper (Cu) exposure. Results emphasize the role of GPxs in the detoxification pathways that, by complex regulation of GPx gene expression, enable Tethraymena to survive in high Cu concentrations and the associated redox environment.

Instability of natural selection at candidate barrier loci underlying speciation in wood ants

Speciation underlies the generation of novel biodiversity. Yet, there is much to learn about how natural selection shapes genomes during speciation. Selection is assumed to act against gene flow at barrier loci, promoting reproductive isolation. However, evidence for gene flow and selection is often indirect and we know very little about the temporal stability of barrier loci. Here we utilize haplodiploidy to identify candidate male barrier loci in hybrids between two wood ant species. As ant males are haploid, they are expected to reveal recessive barrier loci, which can be masked in diploid females if heterozygous. We then test for barrier stability in a sample collected 10 years later and use survival analysis to provide a direct measure of natural selection acting on candidate male barrier loci. We find multiple candidate male barrier loci scattered throughout the genome. Surprisingly, a proportion of them are not stable after 10 years, natural selection apparently switching from acting against to favouring introgression in the later sample. Instability of the barrier effect and natural selection for introgressed alleles could be due to environment-dependent selection, emphasizing the need to consider temporal variation in the strength of natural selection and the stability of the barrier effect at putative barrier loci in future speciation work.

Co-expression clustering across flower development identifies modules for diverse floral forms in Achimenes (Gesneriaceae)

BACKGROUND

Genetic pathways involved with flower color and shape are thought to play an important role in the development of flowers associated with different pollination syndromes, such as those associated with bee, butterfly, or hummingbird pollination. Because pollination syndromes are complex traits that are orchestrated by multiple genes and pathways, the gene regulatory networks have not been explored. Gene co-expression networks provide a systems level approach to identify important contributors to floral diversification.

METHODS

RNA-sequencing was used to assay gene expression across two stages of flower development (an early bud and an intermediate stage) in 10 species of Achimenes (Gesneriaceae). Two stage-specific co-expression networks were created from 9,503 orthologs and analyzed to identify module hubs and the network periphery. Module association with bee, butterfly, and hummingbird pollination syndromes was tested using phylogenetic mixed models. The relationship between network connectivity and evolutionary rates (d N/d S) was tested using linear models.

RESULTS

Networks contained 65 and 62 modules that were largely preserved between developmental stages and contained few stage-specific modules. Over a third of the modules in both networks were associated with flower color, shape, and pollination syndrome. Within these modules, several hub nodes were identified that related to the production of anthocyanin and carotenoid pigments and the development of flower shape. Evolutionary rates were decreased in highly connected genes and elevated in peripheral genes.

DISCUSSION

This study aids in the understanding of the genetic architecture and network properties underlying the development of floral form and provides valuable candidate modules and genes for future studies.

Molecular Chaperones Accelerate the Evolution of Their Protein Clients in Yeast

Protein stability is a major constraint on protein evolution. Molecular chaperones, also known as heat-shock proteins, can relax this constraint and promote protein evolution by diminishing the deleterious effect of mutations on protein stability and folding. This effect, however, has only been stablished for a few chaperones. Here, we use a comprehensive chaperone–protein interaction network to study the effect of all yeast chaperones on the evolution of their protein substrates, that is, their clients. In particular, we analyze how yeast chaperones affect the evolutionary rates of their clients at two very different evolutionary time scales. We first study the effect of chaperone-mediated folding on protein evolution over the evolutionary divergence of Saccharomyces cerevisiae and S. paradoxus. We then test whether yeast chaperones have left a similar signature on the patterns of standing genetic variation found in modern wild and domesticated strains of S. cerevisiae. We find that genes encoding chaperone clients have diverged faster than genes encoding non-client proteins when controlling for their number of protein–protein interactions. We also find that genes encoding client proteins have accumulated more intraspecific genetic diversity than those encoding non-client proteins. In a number of multivariate analyses, controlling by other well-known factors that affect protein evolution, we find that chaperone dependence explains the largest fraction of the observed variance in the rate of evolution at both evolutionary time scales. Chaperones affecting rates of protein evolution mostly belong to two major chaperone families: Hsp70s and Hsp90s. Our analyses show that protein chaperones, by virtue of their ability to buffer destabilizing mutations and their role in modulating protein genotype–phenotype maps, have a considerable accelerating effect on protein evolution.

Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia)

Pleiotropy, the control of multiple phenotypes by a single locus, is expected to slow the rate of adaptation by increasing the chance that beneficial alleles also have deleterious effects. However, a prediction arising from classical theory of quantitative trait evolution states that pleiotropic alleles may have a selective advantage when phenotypes are distant from their selective optima. We examine the role of pleiotropy in regulating adaptive differentiation among populations of common ragweed (Ambrosia artemisiifolia); a species that has recently expanded its North American range due to human-mediated habitat change. We employ a phenotype-free approach by using connectivity in gene networks as a proxy for pleiotropy. First, we identify loci bearing footprints of local adaptation, and then use genotype-expression mapping and co-expression networks to infer the connectivity of the genes. Our results indicate that the putatively adaptive loci are highly pleiotropic, as they are more likely than expected to affect the expression of other genes, and they reside in central positions within the gene networks. We propose that the conditionally advantageous alleles at these loci avoid the cost of pleiotropy by having large phenotypic effects that are beneficial when populations are far from their selective optima. We further use evolutionary simulations to show that these patterns are in agreement with a model where populations face novel selective pressures, as expected during a range expansion. Overall, our results suggest that highly connected genes may be targets of positive selection during environmental change, even though they likely experience strong purifying selection in stable selective environments.

A Catalogue of 59,732 Human-Specific Regulatory Sequences Reveals Unique-to-Human Regulatory Patterns Associated with Virus-Interacting Proteins, Pluripotency, and Brain Development

Extensive searches for genomic regions harboring various types of candidate human-specific regulatory sequences (HSRS) identified thousands' HSRS using high-resolution next-generation sequencing technologies and methodologically diverse comparative analyses of human and nonhuman primates' (NHPs) reference genomes. In this study, a comprehensive catalogue of 59,732 genomic loci harboring candidate HSRS has been assembled to facilitate the systematic analyses of genomic sequences that were either inherited from extinct common ancestors (ECAs) or created de novo in human genomes. These analyses identified thousands of candidate HSRS and HSRS-harboring loci that appear inherited from ECAs, yet absent in genomes of our closest evolutionary relatives, chimpanzee and bonobo, presumably due to the incomplete lineage sorting and/or species-specific loss or regulatory DNA. This pattern is particularly prominent for HSRS-harboring loci that have been putatively associated with human-specific gene expression changes in cerebral organoid models. A prominent majority of regions harboring human-specific mutations associated with human-specific expression changes during brain development is highly conserved in chimpanzee, bonobo, and gorilla genomes. Among NHPs, dominant fractions of HSRS-harboring loci associated with human-specific gene expression in both excitatory neurons (347 loci; 67%) and radial glia (683 loci; 72%) are highly conserved in the gorilla genome. Analysis of 4433 genes encoding virus-interacting proteins (VIPs) revealed that 95.9% of human VIPs are components of human-specific regulatory networks that appear to operate in distinct types of human cells from preimplantation embryos to adult dorsolateral prefrontal cortex. These analyses demonstrate that modern humans captured unique genome-wide combinations of regulatory sequences, divergent subsets of which are highly conserved in distinct species of six NHP separated by 30 million years of evolution. Concurrently, this unique-to-human mosaic of genomic regulatory patterns inherited from ECAs was supplemented with 12,486 created de novo HSRS. Genes encoding VIPs appear to represent a principal genomic target during evolution of human-specific regulatory networks, which contribute to fitness of Homo sapiens and affect a functionally diverse spectrum of biological and cellular processes controlled by VIP-containing liquid-liquid phase-separated condensates.

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.

Evidence that RNA Viruses Drove Adaptive Introgression between Neanderthals and Modern Humans

Neanderthals and modern humans interbred at least twice in the past 100,000 years. While there is evidence that most introgressed DNA segments from Neanderthals to modern humans were removed by purifying selection, less is known about the adaptive nature of introgressed sequences that were retained. We hypothesized that interbreeding between Neanderthals and modern humans led to (1) the exposure of each species to novel viruses and (2) the exchange of adaptive alleles that provided resistance against these viruses. Here, we find that long, frequent-and more likely adaptive-segments of Neanderthal ancestry in modern humans are enriched for proteins that interact with viruses (VIPs). We found that VIPs that interact specifically with RNA viruses were more likely to belong to introgressed segments in modern Europeans. Our results show that retained segments of Neanderthal ancestry can be used to detect ancient epidemics.

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.

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.

The Diverging Routes of BORIS and CTCF: An Interactomic and Phylogenomic Analysis

The CCCTC-binding factor (CTCF) is multi-functional, ubiquitously expressed, and highly conserved from Drosophila to human. It has important roles in transcriptional insulation and the formation of a high-dimensional chromatin structure. CTCF has a paralog called “Brother of Regulator of Imprinted Sites” (BORIS) or “CTCF-like” (CTCFL). It binds DNA at sites similar to those of CTCF. However, the expression profiles of the two proteins are quite different. We investigated the evolutionary trajectories of the two proteins after the duplication event using a phylogenomic and interactomic approach. We find that CTCF has 52 direct interaction partners while CTCFL only has 19. Almost all interactors already existed before the emergence of CTCF and CTCFL. The unique secondary loss of CTCF from several nematodes is paralleled by a loss of two of its interactors, the polycomb repressive complex subunit SuZ12 and the multifunctional transcription factor TYY1. In contrast to earlier studies reporting the absence of BORIS from birds, we present evidence for a multigene synteny block containing CTCFL that is conserved in mammals, reptiles, and several species of birds, indicating that not the entire lineage of birds experienced a loss of CTCFL. Within this synteny block, BORIS and its genomic neighbors seem to be partitioned into two nested chromatin loops. The high expression of SPO11, RAE1, RBM38, and PMEPA1 in male tissues suggests a possible link between CTCFL, meiotic recombination, and fertility-associated phenotypes. Using the 65,700 exomes and the 1000 genomes data, we observed a higher number of intergenic, non-synonymous, and loss-of-function mutations in CTCFL than in CTCF, suggesting a reduced strength of purifying selection, perhaps due to less functional constraint.

High rate of adaptation of mammalian proteins that interact with Plasmodium and related parasites

Plasmodium parasites, along with their Piroplasm relatives, have caused malaria-like illnesses in terrestrial mammals for millions of years. Several Plasmodium-protective alleles have recently evolved in human populations, but little is known about host adaptation to blood parasites over deeper evolutionary timescales. In this work, we analyze mammalian adaptation in ~500 Plasmodium- or Piroplasm- interacting proteins (PPIPs) manually curated from the scientific literature. We show that (i) PPIPs are enriched for both immune functions and pleiotropy with other pathogens, and (ii) the rate of adaptation across mammals is significantly elevated in PPIPs, compared to carefully matched control proteins. PPIPs with high pathogen pleiotropy show the strongest signatures of adaptation, but this pattern is fully explained by their immune enrichment. Several pieces of evidence suggest that blood parasites specifically have imposed selection on PPIPs. First, even non-immune PPIPs that lack interactions with other pathogens have adapted at twice the rate of matched controls. Second, PPIP adaptation is linked to high expression in the liver, a critical organ in the parasite life cycle. Finally, our detailed investigation of alpha-spectrin, a major red blood cell membrane protein, shows that domains with particularly high rates of adaptation are those known to interact specifically with P. falciparum. Overall, we show that host proteins that interact with Plasmodium and Piroplasm parasites have experienced elevated rates of adaptation across mammals, and provide evidence that some of this adaptation has likely been driven by blood parasites.

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.

Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants

Gene coexpression networks are a useful tool for summarizing transcriptomic data and providing insight into patterns of gene regulation in a variety of species. Though there has been considerable interest in studying the evolution of network topology across species, less attention has been paid to the relationship between network position and patterns of molecular evolution. Here, we generated coexpression networks from publicly available expression data for seven flowering plant taxa (Arabidopsis thaliana, Glycine max, Oryza sativa, Populus spp., Solanum lycopersicum, Vitis spp., and Zea mays) to investigate the relationship between network position and rates of molecular evolution. We found a significant negative correlation between network connectivity and rates of molecular evolution, with more highly connected (i.e., “hub”) genes having significantly lower nonsynonymous substitution rates and dN/dS ratios compared to less highly connected (i.e., “peripheral”) genes across the taxa surveyed. These findings suggest that more centrally located hub genes are, on average, subject to higher levels of evolutionary constraint than are genes located on the periphery of gene coexpression networks. The consistency of this result across disparate taxa suggests that it holds for flowering plants in general, as opposed to being a species-specific phenomenon.

Secreted Proteins Defy the Expression Level-Evolutionary Rate Anticorrelation

The rates of evolution of the proteins of any organism vary across orders of magnitude. A primary factor influencing rates of protein evolution is expression. A strong negative correlation between expression levels and evolutionary rates (the so-called E-R anticorrelation) has been observed in virtually all studied organisms. This effect is currently attributed to the abundance-dependent fitness costs of misfolding and unspecific protein-protein interactions, among other factors. Secreted proteins are folded in the endoplasmic reticulum, a compartment where chaperones, folding catalysts, and stringent quality control mechanisms promote their correct folding and may reduce the fitness costs of misfolding. In addition, confinement of secreted proteins to the extracellular space may reduce misinteractions and their deleterious effects. We hypothesize that each of these factors (the secretory pathway quality control and extracellular location) may reduce the strength of the E-R anticorrelation. Indeed, here we show that among human proteins that are secreted to the extracellular space, rates of evolution do not correlate with protein abundances. This trend is robust to controlling for several potentially confounding factors and is also observed when analyzing protein abundance data for 6 human tissues. In addition, analysis of mRNA abundance data for 32 human tissues shows that the E-R correlation is always less negative, and sometimes nonsignificant, in secreted proteins. Similar observations were made in Caenorhabditis elegans and in Escherichia coli, and to a lesser extent in Drosophila melanogaster, Saccharomyces cerevisiae and Arabidopsis thaliana. Our observations contribute to understand the causes of the E-R anticorrelation.

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.

The Relationship between Selection, Network Connectivity, and Regulatory Variation within a Population of Capsella grandiflora

Interactions between genes can have important consequences for how selection shapes sequence variation at these genes. Specifically, genes that have pleiotropic effects by affecting the expression level of many other genes may be under stronger selective constraint. We used coexpression networks to measure connectivity between genes and investigated the relationship between gene connectivity and selection in a natural population of the plant Capsella grandiflora. We observed that network connectivity was negatively correlated with genetic divergence due to stronger negative selection on highly-connected genes even when controlling for variation in gene expression level. However, the presence of local regulatory variation for a gene's expression level was also associated with reduced negative selection and lower gene connectivity. While it is difficult to disentangle the causal relationships between these factors, our results show that both connectivity and local regulatory variation are important factors for explaining variation in selection between genes.

Structural divergence of essential triad ribbon synapse proteins among placental mammals - Implications for preclinical trials in photoreceptor transplantation therapy

As photoreceptor transplantation rapidly moves closer to the clinic, verifying graft efficacy in animal models may have unforeseen xenogeneic barriers. Although photoreceptor transplants have most convincingly exhibited functional synaptogenesis in conspecific studies, such evidence (while ruling out false-positives due to: viral graft labeling, fusion/cytosolic transfer, or neuroprotection) has not yet been shown for discordant xenografts. From this, a fundamental question should be raised: is useful xenosynaptogenesis likely between human photoreceptors and mouse retina? The triad ribbon synapse (TRS) that would normally form is unique and contains trans-synaptic proteins essential to its formation and function. Thus, could interspecific structural divergence be present that may inhibit this trans-synaptic bridge in discordant xenografts? In an effort to address this question computationally, we compared eight recently confirmed (including subcellular location) TRS specific (or predominantly expressed at the TRS) proteins among placental mammals (1-to-1 orthologs) using HyPhy selection analysis (a predictive measure of structural divergence) and by using Phyre2 tertiary structural modeling. Here, selection analysis revealed strong positive (diversifying) selection acting on a particularly important TRS protein: pikachurin. This positive selection was localized to its second Laminin-G (LG)-like domain and on its N-terminal domain - a putative region of trans-synaptic interaction. Localization of structural divergence to the N-terminus of each putative post-translational cleavage (PTC) product may suggest neofunctionalization from ancestral uncleaved pikachurin - this would be consistent with a recent counter-paradigm report of pikachurin cleavage predominating at the TRS. From this, we suggest a dual role after cleavage where the N-terminal fragment can still mediate the trans-synaptic bridge, while the C-terminal fragment may act as a diffusible trophic or "homing" factor for bipolar cell dendrite migration. Tertiary structural models mirrored the conformational divergence predicted by selection analysis. With human and mouse pikachurin (as well as other TRS proteins) likely to diverge considerably in structure among placental mammals - alongside known inter-mammalian variation in TRS phenotype and protein repertoire, high levels of diversifying selection acting on genes involving sensation, considerable timespans allowing for genetic drift that can create xenogeneic epistasis, and uncertainty surrounding the extent of xenosynaptogenesis in PPC transplant studies to date - use of distantly related hosts to test human photoreceptor graft therapeutic efficacy should be considered with caution.

Essentiality Is a Strong Determinant of Protein Rates of Evolution during Mutation Accumulation Experiments in Escherichia coli

The Neutral Theory of Molecular Evolution is considered the most powerful theory to understand the evolutionary behavior of proteins. One of the main predictions of this theory is that essential proteins should evolve slower than dispensable ones owing to increased selective constraints. Comparison of genomes of different species, however, has revealed only small differences between the rates of evolution of essential and nonessential proteins. In some analyses, these differences vanish once confounding factors are controlled for, whereas in other cases essentiality seems to have an independent, albeit small, effect. It has been argued that comparing relatively distant genomes may entail a number of limitations. For instance, many of the genes that are dispensable in controlled lab conditions may be essential in some of the conditions faced in nature. Moreover, essentiality can change during evolution, and rates of protein evolution are simultaneously shaped by a variety of factors, whose individual effects are difficult to isolate. Here, we conducted two parallel mutation accumulation experiments in Escherichia coli, during 5,500–5,750 generations, and compared the genomes at different points of the experiments. Our approach (a short-term experiment, under highly controlled conditions) enabled us to overcome many of the limitations of previous studies. We observed that essential proteins evolved substantially slower than nonessential ones during our experiments. Strikingly, rates of protein evolution were only moderately affected by expression level and protein length.

Viruses are a dominant driver of protein adaptation in mammals

Viruses interact with hundreds to thousands of proteins in mammals, yet adaptation against viruses has only been studied in a few proteins specialized in antiviral defense. Whether adaptation to viruses typically involves only specialized antiviral proteins or affects a broad array of virus-interacting proteins is unknown. Here, we analyze adaptation in ~1300 virus-interacting proteins manually curated from a set of 9900 proteins conserved in all sequenced mammalian genomes. We show that viruses (i) use the more evolutionarily constrained proteins within the cellular functions they interact with and that (ii) despite this high constraint, virus-interacting proteins account for a high proportion of all protein adaptation in humans and other mammals. Adaptation is elevated in virus-interacting proteins across all functional categories, including both immune and non-immune functions. We conservatively estimate that viruses have driven close to 30% of all adaptive amino acid changes in the part of the human proteome conserved within mammals. Our results suggest that viruses are one of the most dominant drivers of evolutionary change across mammalian and human proteomes.

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

Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins) are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons) tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes' adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

Evolution by gene duplication of Medicago truncatula PISTILLATA-like transcription factors

PISTILLATA (PI) is a member of the B-function MADS-box gene family, which controls the identity of both petals and stamens in Arabidopsis thaliana. In Medicago truncatula (Mt), there are two PI-like paralogs, known as MtPI and MtNGL9. These genes differ in their expression patterns, but it is not known whether their functions have also diverged. Describing the evolution of certain duplicated genes, such as transcription factors, remains a challenge owing to the complex expression patterns and functional divergence between the gene copies. Here, we report a number of functional studies, including analyses of gene expression, protein-protein interactions, and reverse genetic approaches designed to demonstrate the respective contributions of each M. truncatula PI-like paralog to the B-function in this species. Also, we have integrated molecular evolution approaches to determine the mode of evolution of Mt PI-like genes after duplication. Our results demonstrate that MtPI functions as a master regulator of B-function in M. truncatula, maintaining the overall ancestral function, while MtNGL9 does not seem to have a role in this regard, suggesting that the pseudogenization could be the functional evolutionary fate for this gene. However, we provide evidence that purifying selection is the primary evolutionary force acting on this paralog, pinpointing the conservation of its biochemical function and, alternatively, the acquisition of a new role for this gene.

The effects of natural selection across molecular pathways in Drosophila melanogaster

BACKGROUND

Whole-genome RNA interference post-transcriptional silencing (RNAi) is a widely used method for studying the phenotypic effects of knocking down individual genes. In this study, we use a population genomic approach to characterize the rate of evolution for proteins affecting 26 RNAi knockdown phenotypes in Drosophila melanogaster.

RESULTS

We find that only two of the 26 RNAi knockdown phenotypes are enriched for rapidly evolving proteins: innate immunity and regulation of Hedgehog signaling. Among all genes associated with an RNAi knockdown phenotype, we note examples in which the adaptively evolving proteins play a well-defined role in a given molecular pathway. However, most adaptively evolving proteins are found to perform more general cellular functions. When RNAi phenotypes are grouped into categories according to cellular function, we find that genes involved in the greatest number of phenotypic categories are also significantly more likely to have a history of rapid protein evolution.

CONCLUSIONS

We show that genes that have been demonstrated to have a measurable effect on multiple molecular phenotypes show higher rates of protein evolution than genes having an effect on a single category of phenotype. Defining pleiotropy in this way yields very different results than previous studies that define pleiotropy by the number of physical interactions, which show highly connected proteins tend to evolve more slowly than lowly connected proteins. We suggest that a high degree of pleiotropy may increase the likelihood of compensatory substitution, consistent with modern theoretical work on adaptation.

Exome sequencing of a colorectal cancer family reveals shared mutation pattern and predisposition circuitry along tumor pathways

The molecular basis of cancer and cancer multiple phenotypes are not yet fully understood. Next Generation Sequencing promises new insight into the role of genetic interactions in shaping the complexity of cancer. Aiming to outline the differences in mutation patterns between familial colorectal cancer cases and controls we analyzed whole exomes of cancer tissues and control samples from an extended colorectal cancer pedigree, providing one of the first data sets of exome sequencing of cancer in an African population against a background of large effective size typically with excess of variants. Tumors showed hMSH2 loss of function SNV consistent with Lynch syndrome. Sets of genes harboring insertions-deletions in tumor tissues revealed, however, significant GO enrichment, a feature that was not seen in control samples, suggesting that ordered insertions-deletions are central to tumorigenesis in this type of cancer. Network analysis identified multiple hub genes of centrality. ELAVL1/HuR showed remarkable centrality, interacting specially with genes harboring non-synonymous SNVs thus reinforcing the proposition of targeted mutagenesis in cancer pathways. A likely explanation to such mutation pattern is DNA/RNA editing, suggested here by nucleotide transition-to-transversion ratio that significantly departed from expected values (p-value 5e-6). NFKB1 also showed significant centrality along with ELAVL1, raising the suspicion of viral etiology given the known interaction between oncogenic viruses and these proteins.