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Natural transformation is the only mechanism of genetic exchange controlled by the recipient bacteria. We quantified its rates in 786 clinical strains of the human pathogens Legionella pneumophila (Lp) and 496 clinical and environmental strains of Acinetobacter baumannii (Ab). The analysis of transformation rates in the light of phylogeny revealed they evolve by a mixture of frequent small changes and a few large quick jumps across 6 orders of magnitude. In standard conditions close to half of the strains of Lp and a more than a third in Ab are below the detection limit and thus presumably non-transformable. Ab environmental strains tend to have higher transformation rates than the clinical ones. Transitions to non-transformability were frequent and usually recent, suggesting that they are deleterious and subsequently purged by natural selection. Accordingly, we find that transformation decreases genetic linkage in both species, which might accelerate adaptation. Intragenomic conflicts with chromosomal mobile genetic elements (MGEs) and plasmids could explain these transitions and a GWAS confirmed systematic negative associations between transformation and MGEs: plasmids and other conjugative elements in Lp, prophages in Ab, and transposable elements in both. In accordance with the hypothesis of modulation of transformation rates by genetic conflicts, transformable strains have fewer MGEs in both species and some MGEs inactivate genes implicated in the transformation with heterologous DNA (in Ab). Innate defense systems against MGEs are associated with lower transformation rates, especially restriction-modification systems. In contrast, CRISPR-Cas systems are associated with higher transformation rates suggesting that adaptive defense systems may facilitate cell protection from MGEs while preserving genetic exchanges by natural transformation. Ab and Lp have different lifestyles, gene repertoires, and population structure. Nevertheless, they exhibit similar trends in terms of variation of transformation rates and its determinants, suggesting that genetic conflicts could drive the evolution of natural transformation in many bacteria. Bacterial species exhibit large variations in their transformation rates. This study of Legionella pneumophila and Acinetobacter baumannii shows that their transformation rates evolve by sudden quick changes, likely caused by intragenomic conflicts between mobile genetic elements and the host bacterium. [ABSTRACT FROM AUTHOR]

Each generation, spontaneous mutations introduce heritable changes that tend to reduce fitness in populations of highly adapted living organisms. This erosion of fitness is countered by natural selection, which keeps deleterious mutations at low frequencies and ultimately removes most of them from the population. The classical way of studying the impact of spontaneous mutations is via mutation accumulation (MA) experiments, where lines of small effective population size are bred for many generations in conditions where natural selection is largely removed. Such experiments in microbes, invertebrates, and plants have generally demonstrated that fitness decays as a result of MA. However, the phenotypic consequences of MA in vertebrates are largely unknown, because no replicated MA experiment has previously been carried out. This gap in our knowledge is relevant for human populations, where societal changes have reduced the strength of natural selection, potentially allowing deleterious mutations to accumulate. Here, we study the impact of spontaneous MA on the mean and genetic variation for quantitative and fitness-related traits in the house mouse using the MA experimental design, with a cryopreserved control to account for environmental influences. We show that variation for morphological and life history traits accumulates at a sufficiently high rate to maintain genetic variation and selection response. Weight and tail length measures decrease significantly between 0.04% and 0.3% per generation with narrow confidence intervals. Fitness proxy measures (litter size and surviving offspring) decrease on average by about 0.2% per generation, but with confidence intervals overlapping zero. When extrapolated to humans, our results imply that the rate of fitness loss should not be of concern in the foreseeable future. Spontaneous mutations tend to reduce fitness in populations of living organisms, but this erosion of fitness is countered by natural selection. This study uses the first mutation accumulation experiment in a mammal to show that even in the absence of natural selection, the rate of fitness loss should not be of concern, which is reassuring for humans. [ABSTRACT FROM AUTHOR]

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information. Author summary: Proteins perform crucial functions in the cell. The biological function of a protein is encoded in its amino-acid sequence. Natural selection acts at the level of function, while mutations arise randomly on sequences. In alignments of sequences of homologous proteins, which share common ancestry and common function, the amino acid usages at different sites can be correlated due to functional constraints. In particular, groups of collectively correlated amino acids, termed sectors, tend to emerge due to selection on functional traits. However, correlations can also arise from the shared evolutionary history of homologous proteins, even without functional constraints. This may obscure the inference of functional sectors. By analyzing controlled synthetic data as well as natural protein sequence data, we show that two very different methods allow to identify sectors and mutational effects in a way that is most robust to phylogeny. We suggest that considering both of these methods allows a better identification of functionally important sites from protein sequences. These results have potential impact on the design of new functional sequences. [ABSTRACT FROM AUTHOR]

Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numerous h values can fit the data, so long as h is greater than ~0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship between h and s can also fit the data well, including models with h = 0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species. Author summary: The dominance coefficient (h) of a mutation determines its impact on organismal fitness when heterozygous. For instance, fully recessive mutations (h = 0) have no effects on fitness when heterozygous whereas additive mutations (h = 0.5) have an effect that is intermediate to the two homozygous genotypes. The extent to which deleterious mutations may be recessive, additive, or dominant is a key area of study in evolutionary genetics. However, dominance parameters remain poorly known in humans and most other organisms due to a variety of technical challenges. In this study, we aim to constrain the possible set of dominance and selection parameters for amino acid changing mutations in humans. We find that a wide range of models are possible, including models with a theoretically-predicted relationship between h and s. We then use a range of plausible selection and dominance models to explore how deleterious variation may have been shaped by the out-of-Africa bottleneck in humans. Our results highlight the subtle influence of dominance on patterns of genetic load in humans and demonstrate that models of partially recessive mutations at amino-acid-changing sites can explain the observed effects of inbreeding on mortality in humans. [ABSTRACT FROM AUTHOR]

In nature, most microbial populations have complex spatial structures that can affect their evolution. Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix. Evolution experiments are beginning to explicitly address the impact of graph structures on mutant fixation. However, the assumptions of evolutionary graph theory differ from the conditions of modern evolution experiments, making the comparison between theory and experiment challenging. Here, we aim to bridge this gap by using our new model of spatially structured populations. This model considers connected subpopulations that lie on the nodes of a graph, and allows asymmetric migrations. It can handle large populations, and explicitly models serial passage events with migrations, thus closely mimicking experimental conditions. We analyze recent experiments in light of this model. We suggest useful parameter regimes for future experiments, and we make quantitative predictions for these experiments. In particular, we propose experiments to directly test our recent prediction that the star graph with asymmetric migrations suppresses natural selection and can accelerate mutant fixation or extinction, compared to a well-mixed population. Author summary: Predicting how mutations spread through a population and eventually take over is important for understanding evolution. Complex spatial structures are ubiquitous in natural microbial populations, and can impact the fate of mutants. Theoretical models have been developed to describe this effect. They predict that some spatial structures have mutant fixation probabilities that differ from those of well-mixed populations. Experiments are beginning to probe these effects in the laboratory. However, there is a disconnect between models and experiments, because they consider different conditions. In this work, we connect them through a new model that closely matches experimental conditions. We analyze recent experiments and propose new ones that should allow testing the effects of complex population spatial structures on mutant fate. [ABSTRACT FROM AUTHOR]

Over the past 2 decades, biologists have come to appreciate that hybridization, or genetic exchange between distinct lineages, is remarkably common—not just in particular lineages but in taxonomic groups across the tree of life. As a result, the genomes of many modern species harbor regions inherited from related species. This observation has raised fundamental questions about the degree to which the genomic outcomes of hybridization are repeatable and the degree to which natural selection drives such repeatability. However, a lack of appropriate systems to answer these questions has limited empirical progress in this area. Here, we leverage independently formed hybrid populations between the swordtail fish Xiphophorus birchmanni and X. cortezi to address this fundamental question. We find that local ancestry in one hybrid population is remarkably predictive of local ancestry in another, demographically independent hybrid population. Applying newly developed methods, we can attribute much of this repeatability to strong selection in the earliest generations after initial hybridization. We complement these analyses with time-series data that demonstrates that ancestry at regions under selection has remained stable over the past approximately 40 generations of evolution. Finally, we compare our results to the well-studied X. birchmanni × X. malinche hybrid populations and conclude that deeper evolutionary divergence has resulted in stronger selection and higher repeatability in patterns of local ancestry in hybrids between X. birchmanni and X. cortezi. How repeatable are the genomic outcomes of hybridization, and how much does natural selection drive such repeatability? This study of hybrid populations of swordtail fish reveals high repeatability due to strong selection in the earliest generations after initial hybridization; deeper evolutionary divergence results in stronger selection and higher repeatability. [ABSTRACT FROM AUTHOR]

Secondary contact between closely related taxa represents a "moment of truth" for speciation—an opportunity to test the efficacy of reproductive isolation that evolved in allopatry and to identify the genetic, behavioral, and/or ecological barriers that separate species in sympatry. Sex chromosomes are known to rapidly accumulate differences between species, an effect that may be exacerbated for neo-sex chromosomes that are transitioning from autosomal to sex-specific inheritance. Here we report that, in the Solomon Islands, two closely related bird species in the honeyeater family—Myzomela cardinalis and Myzomela tristrami—carry neo-sex chromosomes and have come into recent secondary contact after ~1.1 my of geographic isolation. Hybrids of the two species were first observed in sympatry ~100 years ago. To determine the genetic consequences of hybridization, we use population genomic analyses of individuals sampled in allopatry and in sympatry to characterize gene flow in the contact zone. Using genome-wide estimates of diversity, differentiation, and divergence, we find that the degree and direction of introgression varies dramatically across the genome. For sympatric birds, autosomal introgression is bidirectional, with phenotypic hybrids and phenotypic parentals of both species showing admixed ancestry. In other regions of the genome, however, the story is different. While introgression on the Z/neo-Z-linked sequence is limited, introgression of W/neo-W regions and mitochondrial sequence (mtDNA) is highly asymmetric, moving only from the invading M. cardinalis to the resident M. tristrami. The recent hybridization between these species has thus enabled gene flow in some genomic regions but the interaction of admixture, asymmetric mate choice, and/or natural selection has led to the variation in the amount and direction of gene flow at sex-linked regions of the genome. Author summary: When an invasive species colonizes an island and interacts with a closely related resident species, we are provided with a rare opportunity to study the consequences of interbreeding and the factors that keep species distinct. Regions of the genome that evolve rapidly and/or influence mate choice may be especially likely to act as incidental barriers to gene flow. The red Myzomela cardinalis, birds in the honeyeater family, have invaded Makira in the Solomon Islands, coming into contact with the endemic, all black Myzomela tristrami. We used population genomic analyses of individuals in geographic isolation, and those in geographic contact, to understand the history of these two species and the consequences of their recent range overlap. We found that sex-specific regions of the genome (i.e., sex chromosomes) were either limited in their ability to move between species, or only moved in one direction, from the invading M. cardinalis to the resident M. tristrami. This work highlights how certain regions of the genome may be especially important in defining species boundaries and the generation and maintenance of biodiversity. [ABSTRACT FROM AUTHOR]

Silybum marianum (L.) Gaertn. is a multipurpose crop native to the Mediterranean and middle east regions and mainly known for the hepatoprotective properties of fruit-derived silymarin. Despite growing interest in milk thistle as a versatile crop with medicinal value, its potential in agroindustry is hindered by incomplete domestication and limited genomic knowledge, impeding the development of competitive breeding programs. The present study aimed to evaluate genetic diversity in a panel of S. marianum accessions (n = 31), previously characterized for morphological and phytochemical traits, using 5,178 polymorphic DArTseq SNP markers. The genetic structure investigated using both parametric and non-parametric approaches (e.g. PCA, AWclust, Admixture), revealed three distinctive groups reflecting geographical origins. Indeed, Pop1 grouped accessions from Central Europe and UK, Pop3 consisted mainly of accessions of Italian origin, and Pop2 included accessions from different geographical areas. Interestingly, Italian genotypes showed a divergent phenotypic distribution, particularly in fruit oleic and linoleic acid content, compared to the other two groups. Genetic differentiation among the three groups, investigated by computing pairwise fixation index (FST), confirmed a greater differentiation of Pop3 compared to other subpopulations, also based on other diversity indices (e.g. private alleles, heterozygosity). Finally, 22 markers were declared as putatively under natural selection, of which seven significantly affected some important phenotypic traits such as oleic, arachidonic, behenic and linoleic acid content. These findings suggest that these markers, and overall, the seven SNP markers identified within Pop3, could be exploited in specific breeding programs, potentially aimed at diversifying the use of milk thistle. Indeed, incorporating genetic material from Pop3 haplotypes carrying the selected loci into milk thistle breeding populations might be the basis for developing milk thistle lines with higher levels of oleic, arachidonic, and behenic acids, and lower levels of linoleic acid, paving new avenues for enhancing the nutritional and agronomic characteristics of milk thistle. [ABSTRACT FROM AUTHOR]

Like all coronaviruses, infectious bronchitis virus, the causative agent of infectious bronchitis in chickens, exhibits a high mutation rate. Adaptive mutations that arise during the production of live attenuated vaccines against IBV often decrease virulence. The specific impact of these mutations on viral pathogenicity, however, has not been fully elucidated. In this study, we identified a mutation at the 3' end of the S gene in an IBV strain that was serially passaged in chicken embryos, and showed that this mutation resulted in a 9-aa truncation of the cytoplasmic tail (CT) of the S protein. This phenomenon of CT truncation has previously been observed in the production of attenuated vaccines against other coronaviruses such as the porcine epidemic diarrhea virus. We next discovered that the 9-aa truncation in the S protein CT resulted in the loss of the endoplasmic-reticulum-retention signal (KKSV). Rescue experiments with recombinant viruses confirmed that the deletion of the KKSV motif impaired the localization of the S protein to the endoplasmic-reticulum-Golgi intermediate compartment (ERGIC) and increased its expression on the cell surface. This significantly reduced the incorporation of the S protein into viral particles, impaired early subgenomic RNA and protein synthesis, and ultimately reduced viral invasion efficiency in CEK cells. In vivo experiments in chickens confirmed the reduced pathogenicity of the mutant IBV strains. Additionally, we showed that the adaptive mutation altered the TRS-B of ORF3 and impacted the transcriptional regulation of this gene. Our findings underscore the significance of this adaptive mutation in the attenuation of IBV infection and provide a novel strategy for the development of live attenuated IBV vaccines. Author summary: The administration of live attenuated vaccines is currently recognized as the most effective strategy for preventing infections caused by IBV. Understanding how mutations arising during vaccine production influence IBV virulence is crucial for developing more effective vaccines. In this study, we focused on a specific mutation in the S gene of IBV, which arose during viral propagation in chicken embryos. This mutation resulted in the truncation of the CT region of the S protein and concurrently eliminated a critical motif required for the retention of the protein at the viral assembly site. Similar S protein truncation has been observed in the production of attenuated vaccines against other coronaviruses, suggesting that the mutation identified in IBV in the present study was an adaptive mutation driven by natural selection pressure. Our findings underscore the value of targeting this adaptive mutation site as a strategy for reducing IBV virulence, and for the development of effective live attenuated vaccines against IBV and other coronaviruses. [ABSTRACT FROM AUTHOR]

RNA viruses have notoriously high mutation rates due to error-prone replication by their RNA polymerase. However, natural selection concentrates variability in a few key viral proteins. To test whether this stems from different mutation tolerance profiles among viral proteins, we measured the effect of >40,000 non-synonymous mutations across the full proteome of coxsackievirus B3 as well as >97% of all possible codon deletions in the nonstructural proteins. We find significant variation in mutational tolerance within and between individual viral proteins, which correlated with both general and protein-specific structural and functional attributes. Furthermore, mutational fitness effects remained stable across cell lines, suggesting selection pressures are mostly conserved across environments. In addition to providing a rich dataset for understanding virus biology and evolution, our results illustrate that incorporation of mutational tolerance data into druggable pocket discovery can aid in selecting targets with high barriers to drug resistance. RNA viruses replicate with high mutation rates, but natural selection concentrates variability in a few proteins. This study measures the effect of >40,000 non-synonymous mutations across the full proteome of coxsackievirus B3, revealing significant variation in mutational tolerance within and between viral proteins, with implications for drug target selection. [ABSTRACT FROM AUTHOR]

Many social interactions happen indirectly via modifications of the environment, e.g. through the secretion of functional compounds or the depletion of renewable resources. Here, we derive the selection gradient on a quantitative trait affecting dynamical environmental variables that feed back on reproduction and survival in a finite patch-structured population subject to isolation by distance. Our analysis shows that the selection gradient depends on how a focal individual influences the fitness of all future individuals in the population through modifications of the environmental variables they experience, weighted by the neutral relatedness between recipients and the focal. The evolutionarily relevant trait-driven environmental modifications are formalized as the extended phenotypic effects of an individual, quantifying how a trait change in an individual in the present affects the environmental variables in all patches at all future times. When the trait affects reproduction and survival through a payoff function, the selection gradient can be expressed in terms of extended phenotypic effects weighted by scaled relatedness. We show how to compute extended phenotypic effects, relatedness, and scaled relatedness using Fourier analysis, which allow us to investigate a broad class of environmentally mediated social interactions in a tractable way. We use our approach to study the evolution of a trait controlling the costly production of some lasting commons (e.g. a common-pool resource or a toxic compound) that can diffuse in space and persist in time. We show that indiscriminate posthumous spite readily evolves in this scenario. More generally, whether selection favours environmentally mediated altruism or spite is determined by the spatial correlation between an individual's lineage and the commons originating from its patch. The sign of this correlation depends on interactions between dispersal patterns and the commons' renewal dynamics. More broadly, we suggest that selection can favour a wide range of social behaviours when these have carry-over effects in space and time. Author summary: Organisms continually modify their environment by extracting resources, releasing toxins, or engineering habitats. These environmental modifications can have significant fitness consequences on other organisms, including those living far away in space and the distant future. To better understand the evolutionary relevance of such environmentally mediated social interactions, we quantify natural selection on a trait that influences biotic or abiotic environmental variables in a population composed of finite patches experiencing isolation by distance, as is typical of taxa facing limited dispersal. Using Fourier analysis, we show how selection on a trait due to its environmental effects can be understood in terms of the way that a focal individual influences the fitness of all future individuals at all spatial positions via a modification to the environment these individuals encounter, whose importance depends on the relatedness between these and the focal. Simply put, natural selection tends to favour traits whose environmental effects help (resp. harm) individuals that are more (resp. less) related than average, including those distantly in space and time. In addition to allowing for a full quantification of directional selection, this inclusive fitness perspective helps garner intuition for the evolution of environmentally mediated social interactions in general and of spite in particular. [ABSTRACT FROM AUTHOR]

The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects. Author summary: Historically, models of adaptation have typically considered traits as a sum of effects at many genes. Mutations in these genes move a population incrementally closer to an optimum phenotype. However, the genetic basis of traits is often more complex, with interwoven networks of genes creating non-additive effects that might reduce natural selection's ability to steer populations towards optimal trait combinations. In this study, we developed a model that simulates the evolution of a trait as the product of a gene regulatory network. We used this model to compare the effects of the network on adaptation to a typical additive model. We found that mutations in network populations were more likely to drive the population away from an optimum phenotype. We likened this result to the "cost of complexity", where more complicated genetic systems can create constraints on adaptation. Our results suggest that the non-additive interactions emerging from genetic networks might alter the adaptive dynamics predicted under additive models. [ABSTRACT FROM AUTHOR]

This article discusses the importance of genomic surveillance in studying pathogen invasions, using the example of the ash dieback invasion of Europe caused by Hymenoscyphus fraxineus. The ash dieback invasion has had significant economic and ecological impacts, and understanding the genetic variation within the pathogen is crucial for effective management strategies. The article highlights the significance of different types of genomic variation, such as nucleotide substitutions, haplotype diversity, and structural genomic variation, in invasion biology. The authors also discuss various genomic surveillance strategies, including targeted resequencing, whole genome resequencing, and pangenomics, and suggest the potential of air sequencing as a pathogen surveillance method. [Extracted from the article]

Biomorphs, Richard Dawkins's iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations can fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems. Author summary: The question of how novel phenotypic variation arises has received much less attention than natural selection even though both are key processes in Darwinian evolution. Here we uncover striking anisotropic structure in phenotypic variation for biomorphs, Richard Dawkins's iconic schematic model of development, introduced in his book The Blind Watchmaker. We observe strong simplicity bias: upon uniform random sampling of genotypes, a simple biomorph can be exponentially more likely to appear as potential variation than any individual complex biomorph is. The mapping from genotypes to phenotypes (GP map) for biomorphs exhibits many similarities to molecular GP maps, including relatively high mutational phenotype robustness which is positively correlated with evolvability. Such structure in the arrival of variation can enhance the ability of evolution to find fitness maxima through neutral exploration. By using evolutionary simulations, we show that phenotypes that fix in a population may not be the fittest ones, but rather adaptive phenotypes that are most likely to appear as variation. We hypothesize that similar developmental bias as observed in the biomorphs system may occur more widely in developmental systems. [ABSTRACT FROM AUTHOR]

Why and how we age are 2 intertwined questions that have fascinated scientists for many decades. However, attempts to answer these questions remain compartmentalized, preventing a comprehensive understanding of the aging process. We argue that the current lack of knowledge about the evolution of aging mechanisms is due to a lack of clarity regarding evolutionary theories of aging that explicitly involve physiological processes: the disposable soma theory (DST) and the developmental theory of aging (DTA). In this Essay, we propose a new hierarchical model linking genes to vital rates, enabling us to critically reevaluate the DST and DTA in terms of their relationship to evolutionary genetic theories of aging (mutation accumulation (MA) and antagonistic pleiotropy (AP)). We also demonstrate how these 2 theories can be incorporated in a unified hierarchical framework. The new framework will help to generate testable hypotheses of how the hallmarks of aging are shaped by natural selection. Why and how we age are two intertwined questions that have fascinated scientists for many decades. This Essay proposes a new hierarchical model that links genes to vital rates, enabling a critical re-evaluation of the disposable soma theory and the developmental theory of aging in terms of their relationship to evolutionary genetic theories of aging. [ABSTRACT FROM AUTHOR]

Competition among pollen or sperm (gametic selection) can cause evolution. Mating systems shape the intensity of gametic selection by determining the competitors involved, which can in turn cause the mating system itself to evolve. We model the bidirectional relationship between gametic selection and mating systems, focusing on variation in female mating frequency (monandry-polyandry) and self-fertilisation (selfing-outcrossing). First, we find that monandry and selfing both reduce the efficiency of gametic selection in removing deleterious alleles. This means that selfing can increase mutation load, in contrast to cases without gametic selection where selfing purges deleterious mutations and decreases mutation load. Second, we explore how mating systems evolve via their effect on gametic selection. By manipulating gametic selection, polyandry can evolve to increase the fitness of the offspring produced. However, this indirect advantage of post-copulatory sexual selection is weak and is likely to be overwhelmed by any direct fitness effects of mating systems. Nevertheless, gametic selection can be potentially decisive for selfing evolution because it significantly reduces inbreeding depression, which favours selfing. Thus, the presence of gametic selection could be a key factor driving selfing evolution. Author summary: Animals and plants often produce millions of sperm or pollen, of which only a tiny fraction succeed. This means sperm and pollen harbour a lot of evolutionary potential, even though they are generally small and hidden from view. We used mathematical models to understand the evolution of genes that influence the fertilisation success rate of sperm or pollen. A crucial issue is the number of different males who donate sperm or pollen, which is called the mating system. We can predict how genes will evolve with different mating systems. These predictions can then be tested by looking at DNA sequences from genes that are active in sperm or pollen in species with different mating systems. This process allows us to check our understanding of evolution. Secondly, we looked at the evolution of the mating behaviour itself. By changing their mating behaviour, animals and plants can manipulate natural selection on sperm or pollen, thereby evolving to change the way evolution operates. This helps to answer a bigger evolutionary question: why do organisms evolve differences in the way they respond to natural selection? [ABSTRACT FROM AUTHOR]

Horizontal gene transfer (HGT) is a ubiquitous force in microbial evolution. Previous work has shown that the human gut is a hotspot for gene transfer between species, but the more subtle exchange of variation within species—also known as recombination—remains poorly characterized in this ecosystem. Here, we show that the genetic structure of the human gut microbiome provides an opportunity to measure recent recombination events from sequenced fecal samples, enabling quantitative comparisons across diverse commensal species that inhabit a common environment. By analyzing recent recombination events in the core genomes of 29 human gut bacteria, we observed widespread heterogeneities in the rates and lengths of transferred fragments, which are difficult to explain by existing models of ecological isolation or homology-dependent recombination rates. We also show that natural selection helps facilitate the spread of genetic variants across strain backgrounds, both within individual hosts and across the broader population. These results shed light on the dynamics of in situ recombination, which can strongly constrain the adaptability of gut microbial communities. Recombination is a ubiquitous force in bacterial evolution, but its dynamics are poorly characterized in many natural populations. This study presents a metagenomic approach for quantifying the landscape of recombina0on in ~30 common species of human gut bacteria, revealing widespread variation both within and across species. [ABSTRACT FROM AUTHOR]

Genome-wide genealogies of multiple species carry detailed information about demographic and selection processes on individual branches of the phylogeny. Here, we introduce TRAILS, a hidden Markov model that accurately infers time-resolved population genetics parameters, such as ancestral effective population sizes and speciation times, for ancestral branches using a multi-species alignment of three species and an outgroup. TRAILS leverages the information contained in incomplete lineage sorting fragments by modelling genealogies along the genome as rooted three-leaved trees, each with a topology and two coalescent events happening in discretized time intervals within the phylogeny. Posterior decoding of the hidden Markov model can be used to infer the ancestral recombination graph for the alignment and details on demographic changes within a branch. Since TRAILS performs posterior decoding at the base-pair level, genome-wide scans based on the posterior probabilities can be devised to detect deviations from neutrality. Using TRAILS on a human-chimp-gorilla-orangutan alignment, we recover speciation parameters and extract information about the topology and coalescent times at high resolution. Author summary: DNA sequences can be compared to reconstruct the evolutionary history of different species. While the ancestral history is usually represented by a single phylogenetic tree, speciation is a more complex process, and, due to the effect of recombination, different parts of the genome might follow different genealogies. For example, even though humans are more closely related to chimps than to gorillas, around 15% of our genome is more similar to the gorilla genome than to the chimp one. Even for those parts of the genome that do follow the same human-chimp topology, we might encounter a last common ancestor at different time points in the past for different genomic fragments. Here, we present TRAILS, a new framework that utilizes the information contained in all these genealogies to reconstruct the speciation process. TRAILS infers unbiased estimates of the speciation times and the ancestral effective population sizes, improving the accuracy when compared to previous methods. TRAILS also reconstructs the genealogy at the highest resolution, inferring, for example, when common ancestry was found for different parts of the genome. This information can also be used to detect deviations from neutrality, effectively inferring natural selection that happened millions of years ago. We validate the method using extensive simulations, and we apply TRAILS to a human-chimp-gorilla multiple genome alignment, from where we recover speciation parameters that are in good agreement with previous estimates. [ABSTRACT FROM AUTHOR]

Changes in gene regulation have long been appreciated as a driving force of adaptive evolution, however the relative contributions of cis- and trans-acting changes to gene regulation over short evolutionary timescales remain unclear. Instances of recent, parallel phenotypic evolution provide an opportunity to assess whether parallel patterns are seen at the level of gene expression, and to assess the relative contribution of cis- and trans- changes to gene regulation in the early stages of divergence. Here, we studied gene expression in liver and brown adipose tissue in two wild-derived strains of house mice that independently adapted to cold, northern environments, and we compared them to a strain of house mice from a warm, tropical environment. To investigate gene regulatory evolution, we studied expression in parents and allele-specific expression in F1 hybrids of crosses between warm-adapted and cold-adapted strains. First, we found that the different cold-adapted mice showed both unique and shared changes in expression, but that the proportion of shared changes (i.e. parallelism) was greater than expected by chance. Second, we discovered that expression evolution occurred largely at tissue-specific and cis-regulated genes, and that these genes were over-represented in parallel cases of evolution. Finally, we integrated the expression data with scans for selection in natural populations and found substantial parallelism in the two northern populations for genes under selection. Furthermore, selection outliers were associated with cis-regulated genes more than expected by chance; cis-regulated genes under selection influenced phenotypes such as body size, immune functioning, and activity level. These results demonstrate that parallel patterns of gene expression in mice that have independently adapted to cold environments are driven largely by tissue-specific and cis-regulatory changes, providing insight into the mechanisms of adaptive gene regulatory evolution at the earliest stages of divergence. Author summary: The parallel movement of organisms into novel environments provides an opportunity to understand the molecular basis of adaptation and the repeatability of this process. Mutations affecting the expression of genes are known to underlie much of adaptive evolution. Such mutations can arise in cis- (near the gene of interest) or in trans- (at a distant locus), but the relative contribution of these different kinds of changes to adaptation is poorly understood, especially during very recent divergence. Here, we compared evolved gene expression differences between a warm-adapted house mouse strain and two different cold-adapted strains that have independently evolved similar phenotypic traits, such as increased body size and decreased extremity length during the last few hundred years. Using crosses between warm-adapted and cold-adapted mice, we found that mutations acting in a context specific manner (cis-regulatory and tissue-specific changes) predominate expression divergence and are more likely to be involved in parallel evolution. We used population level selection scans in wild animals to identify regions of the genome under selection and combined these findings with the gene expression data to identify candidate genes underlying adaptation to novel environments. Together, our work describes the gene regulatory dynamics of rapid environmental adaptation, and the repeatability of these patterns over multiple instances of adaptation. [ABSTRACT FROM AUTHOR]

Fungal pathogens cause devastating disease in crops. Understanding the evolutionary origin of pathogens is essential to the prediction of future disease emergence and the potential of pathogens to disperse. The fungus Pyrenophora teres f. teres causes net form net blotch (NFNB), an economically significant disease of barley. In this study, we have used 104 P. teres f. teres genomes from four continents to explore the population structure and demographic history of the fungal pathogen. We showed that P. teres f. teres is structured into populations that tend to be geographically restricted to different regions. Using Multiple Sequentially Markovian Coalescent and machine learning approaches we demonstrated that the demographic history of the pathogen correlates with the history of barley, highlighting the importance of human migration and trade in spreading the pathogen. Exploring signatures of natural selection, we identified several population-specific selective sweeps that colocalized with genomic regions enriched in putative virulence genes, and loci previously identified as determinants of virulence specificities by quantitative trait locus analyses. This reflects rapid adaptation to local hosts and environmental conditions of P. teres f. teres as it spread with barley. Our research highlights how human activities can contribute to the spread of pathogens that significantly impact the productivity of field crops. Author summary: Population genetic and genomics studies of several crop pathogens have revealed that human activities, such us domestication, trade, and migration have played a pivotal role in the emergence and spread of plant diseases. In this study, we employed cutting-edge genetic analysis techniques and machine learning tools to shed light on the population structure and historical dispersal patterns of a major fungal pathogen of barley, Pyrenophora teres f. teres. We found that barley domestication during the Neolithic era potentially gave rise to the pathogen which has since co-evolved with barley to become one of the most devastating barley diseases we face today. In addition, we identified a large number of genomic regions evolving under strong positive selection that were specific to different populations. This finding suggests that populations are evolving fast, becoming well adapted to their local host availability and environmental conditions. [ABSTRACT FROM AUTHOR]

Dobzhansky and Muller proposed a general mechanism through which microevolution, the substitution of alleles within populations, can cause the evolution of reproductive isolation between populations and, therefore, macroevolution. As allopatric populations diverge, many combinations of alleles differing between them have not been tested by natural selection and may thus be incompatible. Such genetic incompatibilities often cause low fitness in hybrids between species. Furthermore, the number of incompatibilities grows with the genetic distance between diverging populations. However, what determines the rate and pattern of accumulation of incompatibilities remains unclear. We investigate this question by simulating evolution on holey fitness landscapes on which genetic incompatibilities can be identified unambiguously. We find that genetic incompatibilities accumulate more slowly among genetically robust populations and identify two determinants of the accumulation rate: recombination rate and population size. In large populations with abundant genetic variation, recombination selects for increased genetic robustness and, consequently, incompatibilities accumulate more slowly. In small populations, genetic drift interferes with this process and promotes the accumulation of genetic incompatibilities. Our results suggest a novel mechanism by which genetic drift promotes and recombination hinders speciation. Author summary: As geographically isolated populations evolve, genetic incompatibilities accumulate between them. Eventually, these incompatibilities may cause the populations to become different species. What determines how quickly species are formed in this way remains unclear. We investigate this question using computer simulations and find that genetic incompatibilities accumulate more slowly among populations with individuals that are robust to genetic perturbations. We identify two factors that influence the accumulation rate via their effects on genetic robustness: the size of the populations and how much recombination takes place in them. Small populations with rare recombination accumulate incompatibilities more quickly. [ABSTRACT FROM AUTHOR]

Introgression is a common evolutionary phenomenon that results in shared genetic material across non-sister taxa. Existing statistical methods such as Patterson's D statistic can detect introgression by measuring an excess of shared derived alleles between populations. The D statistic is effective to detect genome-wide patterns of introgression but can give spurious inferences of introgression when applied to local regions. We propose a new statistic, D+, that leverages both shared ancestral and derived alleles to infer local introgressed regions. Incorporating both shared derived and ancestral alleles increases the number of informative sites per region, improving our ability to identify local introgression. We use a coalescent framework to derive the expected value of this statistic as a function of different demographic parameters under an instantaneous admixture model and use coalescent simulations to compute the power and precision of D+. While the power of D and D+ is comparable, D+ has better precision than D. We apply D+ to empirical data from the 1000 Genome Project and Heliconius butterflies to infer local targets of introgression in humans and in butterflies. Author summary: Characterizing how pervasive introgression is across the tree of life is an outstanding question in evolutionary biology. To address this question, we need to detect and quantify introgression to investigate how natural selection has acted on introgressed genetic variation. The D statistic is a widely used method to detect introgression at the genome level, but this method cannot accurately detect introgression locally in the genome. To improve its performance at the local level, we incorporate ancestral variation shared between the donor and recipient populations. We show, theoretically and with simulations, that re-introduced ancestral alleles into the recipient population also contain information to detect introgression. Using both shared derived and ancestral variation, we define a new statistic, D+, that can be used to detect the location of introgressed regions in a genome with a history of introgression. [ABSTRACT FROM AUTHOR]

The Japanese archipelago is a terminal location for human migration, and the contemporary Japanese people represent a unique population whose genomic diversity has been shaped by multiple migrations from Eurasia. We analyzed the genomic characteristics that define the genetic makeup of the modern Japanese population from a population genetics perspective from the genomic data of 9,287 samples obtained by high-coverage whole-genome sequencing (WGS) by the National Center Biobank Network. The dataset comprised populations from the Ryukyu Islands and other parts of the Japanese archipelago (Hondo). The Hondo population underwent two episodes of population decline during the Jomon period, corresponding to the Late Neolithic, and the Edo period, corresponding to the Early Modern era, while the Ryukyu population experienced a population decline during the shell midden period of the Late Neolithic in this region. Haplotype analysis suggested increased allele frequencies for genes related to alcohol and fatty acid metabolism, which were reported as loci that had experienced positive natural selection. Two genes related to alcohol metabolism were found to be 12,500 years out of phase with the time when they began to increase in the allele frequency; this finding indicates that the genomic diversity of Japanese people has been shaped by events closely related to agriculture and food production. Author summary: The human population in the Japanese archipelago exhibits significant genetic diversity, with the Ryukyu Islands and other parts of the archipelago (Hondo) having undergone distinct evolutionary paths that have contributed to the genetic divergence of the populations in each region. In this study, whole genome sequencing of healthy individuals from national research hospital biobanks was utilized to investigate the genetic diversity of the Japanese population. Haplotypes were inferred from the genomic data, and a thorough population genetic analysis was conducted. The results indicated not only genetic differentiation between Hondo and the Ryukyu Islands, but also marked differences in past population size. In addition, gene genealogies were inferred from the haplotypes, and the patterns were scrutinized for evidence of natural selection. This analysis revealed unique traces of natural selection in East Asian populations, many of which were believed to be linked to dietary changes brought about by agriculture and food production. [ABSTRACT FROM AUTHOR]

Admixture, the exchange of genetic information between distinct source populations, is thought to be a major source of adaptive genetic variation. Unlike mutation events, which periodically generate single alleles, admixture can introduce many selected alleles simultaneously. As such, the effects of linkage between selected alleles may be especially pronounced in admixed populations. However, existing tools for identifying selected mutations within admixed populations only account for selection at a single site, overlooking phenomena such as linkage among proximal selected alleles. Here, we develop and extensively validate a method for identifying and quantifying the individual effects of multiple linked selected sites on a chromosome in admixed populations. Our approach numerically calculates the expected local ancestry landscape in an admixed population for a given multi-locus selection model, and then maximizes the likelihood of the model. After applying this method to admixed populations of Drosophila melanogaster and Passer italiae, we found that the impacts between linked sites may be an important contributor to natural selection in admixed populations. Furthermore, for the situations we considered, the selection coefficients and number of selected sites are overestimated in analyses that do not consider the effects of linkage among selected sites. Our results imply that linkage among selected sites may be an important evolutionary force in admixed populations. This tool provides a powerful generalized method to investigate these crucial phenomena in diverse populations. Author summary: In this text we introduce a method for modeling the transition rates between local ancestry states at sites along a genome which may be in linkage to multiple sites under selection. We implement our method as a tool called AHMM_MLS which we validate with simulations and apply to admixed systems of Drosophila melanogaster and Passer italiae. In our validations we found that our tool can accurately detect the number of sites and predict their selection coefficients even when they are in linkage. We also found that the inferred selection coefficients of selected sites in linkage may be overestimated if the effects of linkage are ignored. In applying our tool to two admixed systems, we found that our lab's previous tool AHMM-S may overestimate the number and selection coefficients of selected positions along chromosomes in these populations. Our multi-locus selection model for these admixed chromosomes produces an expected local ancestry that closely matches the local ancestry inferred from samples. We expect that our tool will allow greater insights into admixed chromosomes which show signs of a few strong selected positions that are in linkage. [ABSTRACT FROM AUTHOR]

Network interpretation illuminates our understanding of the dynamic nature of cultural evolution. Guided by cultural evolution theory, this article explores how people collectively develop knowledge through knowledge collaboration network traits. Using network data from 910 artifacts (the WikiProject Aquarium Fishes articles) over 163 weeks, two studies were designed to understand how collaboration network traits drive population and artifact-level knowledge evolution. The first study examines the selection pressure imposed by10 network traits (against 11 content traits) on population-level evolutionary outcomes. While network traits are vital in identifying natural selection pressure, intriguingly, no significant difference was found between network traits and content traits, challenging a recent theory on network-driven evolution. The second study utilizes time series analysis to reveal that three network traits (embeddedness, connectivity, and redundancy) at a prior time predict future artifact development trajectory. This implies that people collectively explore various positions in a potential solution space, suggesting content exploration as a possible explanation of knowledge evolution. In summary, understanding the interplay between network traits and content exploration provides valuable insights into the mechanisms driving knowledge evolution and offers new avenues for future research. [ABSTRACT FROM AUTHOR]

The mutational processes dictating the accumulation of mutations in genomes are shaped by genetic background, environment and their interactions. Accurate quantification of mutation rates and spectra under drugs has important implications in disease treatment. Here, we used whole-genome sequencing and time-resolved growth phenotyping of yeast mutation accumulation lines to give a detailed view of the mutagenic effects of rapamycin and hydroxyurea on the genome and cell growth. Mutation rates depended on the genetic backgrounds but were only marginally affected by rapamycin. As a remarkable exception, rapamycin treatment was associated with frequent chromosome XII amplifications, which compensated for rapamycin induced rDNA repeat contraction on this chromosome and served to maintain rDNA content homeostasis and fitness. In hydroxyurea, a wide range of mutation rates were elevated regardless of the genetic backgrounds, with a particularly high occurrence of aneuploidy that associated with dramatic fitness loss. Hydroxyurea also induced a high T-to-G and low C-to-A transversion rate that reversed the common G/C-to-A/T bias in yeast and gave rise to a broad range of structural variants, including mtDNA deletions. The hydroxyurea mutation footprint was consistent with the activation of error-prone DNA polymerase activities and non-homologues end joining repair pathways. Taken together, our study provides an in-depth view of mutation rates and signatures in rapamycin and hydroxyurea and their impact on cell fitness, which brings insights for assessing their chronic effects on genome integrity. Author summary: As the ultimate source of genetic variation, mutation plays critical roles in evolution. An accurate depiction of its intrinsic rate and signature can help us understand the genetic basis of biodiversity and diseases. However, the ubiquitous existence of natural selection often leads to bias for the observable mutations in natural populations. To minimize such confounding effect introduced by selection, we applied evolution experiment by random single-cell bottlenecks, which allows almost all kinds of mutations to accumulate in an unbiased way. With this setup, we examined the mutation rates and signatures of yeast cells in two commonly used chemotherapy drugs that impairs essential cellular functions such as DNA and protein synthesis. We found elevated mutation rates for a wide range of genetic variants, accompanied by dramatic fitness loss in hydroxyurea. The mutational signatures suggest the involvement of low fidelity DNA replication and repair processes. The mutagenic effects of rapamycin are marginal but with frequent chromosome XII amplifications that compensate for rapamycin-induced rDNA contraction on this chromosome. Our findings provide an example of how such experiments on model organisms can help us better understand the chronic mutagenic effects of drugs and their underlying biological mechanisms. [ABSTRACT FROM AUTHOR]

The ability to perform genomic sequencing on long-dead organisms is opening new frontiers in evolutionary research. These opportunities are especially notable in the case of museum collections, from which countless documented specimens may now be suitable for genomic analysis—if data of sufficient quality can be obtained. Here, we report 25 newly sequenced genomes from museum specimens of the model organism Drosophila melanogaster, including the oldest extant specimens of this species. By comparing historical samples ranging from the early 1800s to 1933 against modern-day genomes, we document evolution across thousands of generations, including time periods that encompass the species' initial occupation of northern Europe and an era of rapidly increasing human activity. We also find that the Lund, Sweden population underwent local genetic differentiation during the early 1800s to 1933 interval (potentially due to drift in a small population) but then became more similar to other European populations thereafter (potentially due to increased migration). Within each century-scale time period, our temporal sampling allows us to document compelling candidates for recent natural selection. In some cases, we gain insights regarding previously implicated selection candidates, such as ChKov1, for which our inferred timing of selection favors the hypothesis of antiviral resistance over insecticide resistance. Other candidates are novel, such as the circadian-related gene Ahcy, which yields a selection signal that rivals that of the DDT resistance gene Cyp6g1. These insights deepen our understanding of recent evolution in a model system, and highlight the potential of future museomic studies. Genomes from 25 museum specimens of Drosophila melanogaster (some more than 200 years old) reveal that small populations occupying northern Europe gave way to well-connected fly populations across the continent, and also illuminate targets of recent selection, including genes that may have helped this species adapt to novel climates, viruses, and insecticides. [ABSTRACT FROM AUTHOR]

In the formation of species, adaptation by natural selection generates distinct combinations of traits that function well together. The maintenance of adaptive trait combinations in the face of gene flow depends on the strength and nature of selection acting on the underlying genetic loci. Floral pollination syndromes exemplify the evolution of trait combinations adaptive for particular pollinators. The North American wildflower genus Penstemon displays remarkable floral syndrome convergence, with at least 20 separate lineages that have evolved from ancestral bee pollination syndrome (wide blue-purple flowers that present a landing platform for bees and small amounts of nectar) to hummingbird pollination syndrome (bright red narrowly tubular flowers offering copious nectar). Related taxa that differ in floral syndrome offer an attractive opportunity to examine the genomic basis of complex trait divergence. In this study, we characterized genomic divergence among 229 individuals from a Penstemon species complex that includes both bee and hummingbird floral syndromes. Field plants are easily classified into species based on phenotypic differences and hybrids displaying intermediate floral syndromes are rare. Despite unambiguous phenotypic differences, genome-wide differentiation between species is minimal. Hummingbird-adapted populations are more genetically similar to nearby bee-adapted populations than to geographically distant hummingbird-adapted populations, in terms of genome-wide dXY. However, a small number of genetic loci are strongly differentiated between species. These approximately 20 "species-diagnostic loci," which appear to have nearly fixed differences between pollination syndromes, are sprinkled throughout the genome in high recombination regions. Several map closely to previously established floral trait quantitative trait loci (QTLs). The striking difference between the diagnostic loci and the genome as whole suggests strong selection to maintain distinct combinations of traits, but with sufficient gene flow to homogenize the genomic background. A surprisingly small number of alleles confer phenotypic differences that form the basis of species identity in this species complex. A genomic study of 229 individuals from a Penstemon species complex that includes both bee and hummingbird pollination syndromes reveals that although genome-wide differentiation between species is minimal, about 20 "species-diagnostic loci," with fixed differences between syndromes, are sprinkled throughout the genome in high recombination regions. [ABSTRACT FROM AUTHOR]

SLC30A9 encodes a ubiquitously zinc transporter (ZnT9) and has been consistently suggested as a candidate for positive selection in humans. However, no direct adaptive molecular phenotype has been demonstrated. Our results provide evidence for directional selection operating in two major complementary haplotypes in Africa and East Asia. These haplotypes are associated with differential gene expression but also differ in the Met50Val substitution (rs1047626) in ZnT9, which we show is found in homozygosis in the Denisovan genome and displays accompanying signatures suggestive of archaic introgression. Although we found no significant differences in systemic zinc content between individuals with different rs1047626 genotypes, we demonstrate that the expression of the derived isoform (ZnT9 50Val) in HEK293 cells shows a gain of function when compared with the ancestral (ZnT9 50Met) variant. Notably, the ZnT9 50Val variant was found associated with differences in zinc handling by the mitochondria and endoplasmic reticulum, with an impact on mitochondrial metabolism. Given the essential role of the mitochondria in skeletal muscle and since the derived allele at rs1047626 is known to be associated with greater susceptibility to several neuropsychiatric traits, we propose that adaptation to cold may have driven this selection event, while also impacting predisposition to neuropsychiatric disorders in modern humans. Author summary: Contrasting continental signatures of positive natural selection have been previously found in the human SLC30A9 gene encoding the protein ZnT9, which transports zinc across cell membranes. Here we investigate the genetic variants that have been targeted by natural selection in the surrounding region of this gene and which molecular and whole-body changes may have brought about. We found that two major SLC30A9 variant combinations (haplotypes) that are extremely frequent in Africa and East Asia, respectively, are expressed differentially. These two haplotypes also differ at one site that creates an amino acid difference at ZnT9; the version most often found outside Africa avoiding zinc overload in the endoplasmic reticulum and mitochondria and directly influencing mitochondrial activity. Moreover, we found that this substitution, which is known to be associated with greater susceptibility to several neuropsychiatric disorders, is present in the Denisova and displays accompanying patterns of variation that could be suggestive of adaptive introgression. Since mitochondria play an important role in skeletal muscle energy metabolism, we speculate that adaptation to cold may have driven this selection event outside Africa, while also impacting predisposition to neuropsychiatric disorders in modern humans. [ABSTRACT FROM AUTHOR]

Hypergraphs have been a useful tool for analyzing population dynamics such as opinion formation and the public goods game occurring in overlapping groups of individuals. In the present study, we propose and analyze evolutionary dynamics on hypergraphs, in which each node takes one of the two types of different but constant fitness values. For the corresponding dynamics on conventional networks, under the birth-death process and uniform initial conditions, most networks are known to be amplifiers of natural selection; amplifiers by definition enhance the difference in the strength of the two competing types in terms of the probability that the mutant type fixates in the population. In contrast, we provide strong computational evidence that a majority of hypergraphs are suppressors of selection under the same conditions by combining theoretical and numerical analyses. We also show that this suppressing effect is not explained by one-mode projection, which is a standard method for expressing hypergraph data as a conventional network. Our results suggest that the modeling framework for structured populations in addition to the specific network structure is an important determinant of evolutionary dynamics, paving a way to studying fixation dynamics on higher-order networks including hypergraphs. Author summary: Evolutionary dynamics describes spreading and competition of different types of individuals in a population. Prior research has revealed that the population structure, which is typically modeled by networks, is a key factor that affects evolutionary dynamics. Hypergraphs are a generalization of networks and model a set of groups in a population in which a group can involve more than two individuals who simultaneously interact, differently from conventional networks. In the present study, we ask a key question: do hypergraphs yield evolutionary dynamics that are drastically different from those on conventional networks? We have found that the hypergraphs that we have examined are suppressors of natural selection, which discounts the strength of the stronger type towards neutrality. This result is surprising because most conventional networks are amplifiers of natural selection, which magnifies the strength of the stronger type, under the same conditions. Our results suggest that how we model population structure in addition to the specific network structure is an important determinant of evolutionary dynamics. [ABSTRACT FROM AUTHOR]

Objectives: The present paper aims to characterize the Angiotensin-converting enzyme (ACE) genotype, with particular emphasis on its association with arterial oxygen saturation, arterial blood pressure, hemoglobin [Hb] concentration, and ventilatory measures among the Tawang Monpa, a high-altitude native population of the Eastern Himalaya, India. Methods: A cross-sectional sample of 168Monpa participants from Tawang town, Arunachal Pradesh, India, was selected who live at an altitude of ∼3,200 meters (m) above sea level. For each participant, height, weight, and skinfold thickness were measured, based on which body mass index (BMI, kg/m2) and percentage of body fat (%BF) were calculated. Physiological measures, such as the transcutaneous arterial oxygen saturation (SaO2), hemoglobin [Hb] concentration, forced vital capacity (FVC), forced expiratory volume in 1-second (FEV1), and systemic arterial blood pressure were measured. First, the peripheral venous blood samples (four ml) were drawn, and then white blood cells were separated for the ACE genotyping of each participant. Results: Unlike high-altitude natives from Peru and Ladakh, who exhibit high frequencies of II homozygotes, the Tawang Monpa shows a significantly high frequency of ID heterozygotes (p<0.0001). In addition, no significant association was identified between ACE gene polymorphism and arterial blood pressure, oxygen saturation at rest, vital capacity, or [Hb] concentration. Discussion: The results suggest that the association of the ACE gene with resting SaO2 is inconsistent across native populations living under hypobaric hypoxia. Further, ACE I/D gene polymorphism may not be under natural selection in specific native populations, including Tawang Monpa, for their adaptation to high-altitude hypoxia. [ABSTRACT FROM AUTHOR]

Plant pathogens secrete proteins called effectors that target host cellular processes to promote disease. Recently, structural genomics has identified several families of fungal effectors that share a similar three-dimensional structure despite remarkably variable amino-acid sequences and surface properties. To explore the selective forces that underlie the sequence variability of structurally-analogous effectors, we focused on MAX effectors, a structural family of effectors that are major determinants of virulence in the rice blast fungus Pyricularia oryzae. Using structure-informed gene annotation, we identified 58 to 78 MAX effector genes per genome in a set of 120 isolates representing seven host-associated lineages. The expression of MAX effector genes was primarily restricted to the early biotrophic phase of infection and strongly influenced by the host plant. Pangenome analyses of MAX effectors demonstrated extensive presence/absence polymorphism and identified gene loss events possibly involved in host range adaptation. However, gene knock-in experiments did not reveal a strong effect on virulence phenotypes suggesting that other evolutionary mechanisms are the main drivers of MAX effector losses. MAX effectors displayed high levels of standing variation and high rates of non-synonymous substitutions, pointing to widespread positive selection shaping the molecular diversity of MAX effectors. The combination of these analyses with structural data revealed that positive selection acts mostly on residues located in particular structural elements and at specific positions. By providing a comprehensive catalog of amino acid polymorphism, and by identifying the structural determinants of the sequence diversity, our work will inform future studies aimed at elucidating the function and mode of action of MAX effectors. Author summary: Fungal plant pathogens use small secreted proteins, called effectors, to manipulate to their own advantage their host's physiology and immunity. The evolution of these effectors, whether spontaneously or in response to human actions, can lead to epidemics or the emergence of new diseases. It is therefore crucial to understand the mechanisms underlying this evolution. In this article, we report on the evolution of effectors in one of the prime experimental model systems of plant pathology, Pyricularia oryzae, the fungus causing blast diseases in rice, wheat, and other cereals or grasses. We further characterize in this fungus a particular class of effectors, called MAX effectors, using structural models based on experimental protein structures of effectors. We show that this class of effector is produced by the pathogen during the early stages of infection, when plant cells are still alive. By comparing the gene content of isolates infecting different plant species, we show that the MAX effector arsenal is highly variable from one isolate to another. Finally, using the inferential framework of population genetics, we demonstrate that MAX effectors exhibit very high genetic variability and that this results from the action of natural selection. [ABSTRACT FROM AUTHOR]

How various factors, including demography, recombination or genome duplication, may impact the efficacy of natural selection and the burden of deleterious mutations,is a central question in evolutionary biology and genetics. In this study, we show that key evolutionary processes, including variations in i) effective population size (Ne) ii) recombination rates and iii) chromosome inheritance, have influenced the genetic load and efficacy of selection in Coho salmon (Oncorhynchus kisutch), a widely distributed salmonid species on the west coast of North America. Using whole genome resequencing data from 14 populations at different migratory distances from their southern glacial refugium, we found evidence supporting gene surfing, wherein reduced Ne at the postglacial recolonization front, leads to a decrease in the efficacy of selection and a surf of deleterious alleles in the northernmost populations. Furthermore, our results indicate that recombination rates play a prime role in shaping the load along the genome. Additionally, we identified variation in polyploidy as a contributing factor to within-genome variation of the load. Overall, our results align remarkably well with expectations under the nearly neutral theory of molecular evolution. We discuss the fundamental and applied implications of these findings for evolutionary and conservation genomics. Author summary: Understanding how historical processes, such as past glaciations, may have impacted variations in population size and genetic diversity along the genome is a fundamental question in evolution. In this study, we investigated how recent postglacial demographic expansion has affected the distribution of deleterious genetic variants and the resulting deleterious mutation load in Coho salmon (Oncorhynchus kisutch), throughout its native range in North America. By sequencing the entire genome of 71 Coho salmon, we reveal that postglacial expansion has led to allele surfing, a process where alleles increase in frequency in populations that are expanding or colonizing new environments. Here, allele surfing resulted in an increased deleterious mutation load at the colonization front. Furthermore, we demonstrated that the efficacy of natural selection scales with variation in effective population size among populations. We showed that the specific genomic features of Coho salmon, namely variation in local recombination rate and variation in chromosomal inheritance, strongly impacted the segregation of deleterious mutations. [ABSTRACT FROM AUTHOR]

Here we propose that viruses emerging in the human population undergo an evolution that is conditioned by the rules of chaos. Our data support the notion that the initial growth rate "r" affects the chances of the virus to establish a long-lasting relationship with the new host. Indeed, an emerging virus is able to spread and adapt only when it displays an initial r falling in a range frankly associated with chaotic growth. [ABSTRACT FROM AUTHOR]

Viruses with single-stranded, positive-sense (+) RNA genomes incur high numbers of errors during replication, thereby creating diversified genome populations from which new, better adapted viral variants can emerge. However, a definitive error rate is known for a relatively few (+) RNA plant viruses, due to challenges to account for perturbations caused by natural selection and/or experimental set-ups. To address these challenges, we developed a new approach that exclusively profiled errors in the (-)-strand replication intermediates of turnip crinkle virus (TCV), in singly infected cells. A series of controls and safeguards were devised to ensure errors inherent to the experimental process were accounted for. This approach permitted the estimation of a TCV error rate of 8.47 X 10−5 substitution per nucleotide site per cell infection. Importantly, the characteristic error distribution pattern among the 50 copies of 2,363-base-pair cDNA fragments predicted that nearly all TCV (-) strands were products of one replication cycle per cell. Furthermore, some of the errors probably elevated error frequencies by lowering the fidelity of TCV RNA-dependent RNA polymerase, and/or permitting occasional re-replication of progeny genomes. In summary, by profiling errors in TCV (-)-strand intermediates incurred during replication in single cells, this study provided strong support for a stamping machine mode of replication employed by a (+) RNA virus. Author summary: Most (+) RNA viruses introduce replication errors at relatively high frequencies. As a result, it is of vital importance for these viruses to purge lethal errors in a timely manner. TCV, a plant-infecting small (+) RNA virus, was proposed to encode a Bottleneck, Isolate, Amplify, Select (BIAS) mechanism that compel swift clearance of lethal errors by bottlenecking the number of replicating genome copies to one per cell. A crucial prediction of this BIAS model is that such bottlenecking also acts on progeny genome copies, preventing them from repeating replication in the cells of their own genesis. The current study tested this prediction by developing a carefully controlled, readily reproducible approach to profile errors and error distributions in (-)-stranded replication intermediates of TCV. We found that most of replication-generated (-) strands descended from the primary (+) strands through a single replication cycle. This finding adds fresh support to the BIAS model. [ABSTRACT FROM AUTHOR]

Antibiotic resistance is a growing global concern in the field of medicine as it renders bacterial infections difficult to treat and often more severe. Acinetobacter baumannii is a gram-negative bacterial pathogen causing a wide range of infections, including pneumonia, sepsis, urinary tract infections, and wound infections. A. baumannii has emerged as a significant healthcare-associated pathogen due to its high level of antibiotic resistance. The global spread of antibiotic-resistant strains of A. baumannii has resulted in limited treatment options, leading to increased morbidity and mortality rates, especially in vulnerable populations such as the elderly and immunocompromised individuals, as well as longer hospital stays and higher healthcare costs. Further complicating the situation, multi- and pan-drug-resistant strains of A. baumannii are becoming increasingly common, and these deadly strains are resistant to all or almost all available antibiotics. A. baumannii employs various clever strategies to develop antibiotic resistance, including horizontal transfer of resistance genes, overexpression of inherent efflux pumps that remove drugs from the cell, intrinsic mutations, combined with natural selection under antibiotic selective pressure leading to emergence of successful resistance clones. The typical multidrug resistance phenotype of A. baumannii is, therefore, an orchestrated collimation of all these mechanisms combined with the worldwide spread of "global clones," rendering infections caused by this pathogen challenging to control and treat. To address the escalating problem of antibiotic resistance in A. baumannii, there is a need for increased surveillance, strict infection control measures, and the development of new treatment strategies, requiring a concerted effort by healthcare professionals, researchers, and policymakers. [ABSTRACT FROM AUTHOR]

The genetic causes of phenotypic variation often differ depending on the population examined, particularly if the populations were founded by relatively small numbers of genotypes. Similarly, the genetic causes of phenotypic variation among similar traits (resistance to different xenobiotic compounds or pathogens) may also be completely different or only partially overlapping. Differences in genetic causes for variation in the same trait among populations suggests context dependence for how selection acts on those traits. Similarities in the genetic causes of variation for different traits, on the other hand, suggests pleiotropy which would also influence how natural selection shapes variation in a trait. We characterized immune defense against a natural Drosophila pathogen, the Gram-positive bacterium Lysinibacillus fusiformis, in three different populations and found almost no overlap in the genetic architecture of variation in survival post infection. However, when comparing our results to a similar experiment with the fungal pathogen, B. bassiana, we found a convincing shared QTL peak for both pathogens. This peak contains the Bomanin cluster of Drosophila immune effectors. Loss of function mutants and RNAi knockdown experiments confirms a role of some of these genes in immune defense against both pathogens. This suggests that natural selection may act on the entire cluster of Bomanin genes (and the linked region under the QTL) or specific peptides for specific pathogens. Author summary: Like most traits, the way individuals respond to infection vary among individuals within a population. Some of this variation is caused by genetic differences in the host. Over the past decade, two prominent resources were developed to assess genetic variation for complex traits of the fruit fly, Drosophila melanogaster and map the genetic variants responsible. We recently described a strain of Lysinibacillus fusiformis bacteria that was isolated from fruit flies and is moderately virulent when flies are infected via septic injury. We mapped genetic variation in resistance L. fusiformis using these mapping resources. We find that among the resources, different genetic changes were associated with immune defense. However, we also found that within a resource, the same region of the genome was associated with resistance to both L. fusiformis and a fungal pathogen. These results suggest that different populations adapt differently to the same pathogens, but two distinct pathogens share similar causes of genetic variation within a single population. [ABSTRACT FROM AUTHOR]

Reshuffling of genetic variation occurs both by independent assortment of chromosomes and by homologous recombination. Such reshuffling can generate novel allele combinations and break linkage between advantageous and deleterious variants which increases both the potential and the efficacy of natural selection. Here we used high-density linkage maps to characterize global and regional recombination rate variation in two populations of the wood white butterfly (Leptidea sinapis) that differ considerably in their karyotype as a consequence of at least 27 chromosome fissions and fusions. The recombination data were compared to estimates of genetic diversity and measures of selection to assess the relationship between chromosomal rearrangements, crossing over, maintenance of genetic diversity and adaptation. Our data show that the recombination rate is influenced by both chromosome size and number, but that the difference in the number of crossovers between karyotypes is reduced as a consequence of a higher frequency of double crossovers in larger chromosomes. As expected from effects of selection on linked sites, we observed an overall positive association between recombination rate and genetic diversity in both populations. Our results also revealed a significant effect of chromosomal rearrangements on the rate of intergenic diversity change between populations, but limited effects on polymorphisms in coding sequence. We conclude that chromosomal rearrangements can have considerable effects on the recombination landscape and consequently influence both maintenance of genetic diversity and efficiency of selection in natural populations. Author summary: Reshuffling genetic variation is fundamental for maintaining genetic diversity and creating novel allelic combinations. The two main processes involved are the independent assortment of chromosomes and homologous recombination. The number and size of chromosomes can influence the amount of pairwise reshuffling and local recombination patterns. However, studying this in natural populations is challenging. In this study, we used the wood white butterfly, which exhibits an extreme within-species karyotype difference. Extensive fusions and fissions have resulted in almost twice as many chromosomes in the southern populations compared to the northeast populations. This unique system allowed us to assess the relationship between karyotype differences, pairwise reshuffling, recombination rate variation and subsequent effects on diversity and linked selection. We found that a higher number of chromosomes results in higher number of crossovers, although the difference was less than expected due to multiple recombination events occurring on longer chromosomes. Both populations showed an association between recombination rate and genome-wide patterns of genetic diversity and efficacy of selection. We provide evidence that chromosomal rearrangements have considerable effects on the recombination landscape and thereby influence the maintenance of genetic diversity in populations. [ABSTRACT FROM AUTHOR]

Most organisms are under constant and repeated exposure to pathogens, leading to perpetual natural selection for more effective ways to fight-off infections. This could include the evolution of memory-based immunity to increase protection from repeatedly-encountered pathogens both within and across generations. There is mixed evidence for intra- and trans-generational priming in non-vertebrates, which lack the antibody-mediated acquired immunity characteristic of vertebrates. In this work, we tested for trans-generational immune priming in adult offspring of the fruit fly, Drosophila melanogaster, after maternal challenge with 10 different bacterial pathogens. We focused on natural opportunistic pathogens of Drosophila spanning a range of virulence from 10% to 100% host mortality. We infected mothers via septic injury and tested for enhanced resistance to infection in their adult offspring, measured as the ability to suppress bacterial proliferation and survive infection. We categorized the mothers into four classes for each bacterium tested: those that survived infection, those that succumbed to infection, sterile-injury controls, and uninjured controls. We found no evidence for trans-generational priming by any class of mother in response to any of the bacteria. [ABSTRACT FROM AUTHOR]

Viruses are constantly subject to natural selection to enrich beneficial mutations and weed out deleterious ones. However, it remains unresolved as to how the phenotypic gains or losses brought about by these mutations cause the viral genomes carrying the very mutations to become more or less numerous. Previous investigations by us and others suggest that viruses with plus strand (+) RNA genomes may compel such selection by bottlenecking the replicating genome copies in each cell to low single digits. Nevertheless, it is unclear if similarly stringent reproductive bottlenecks also occur in cells invaded by DNA viruses. Here we investigated whether tomato yellow leaf curl virus (TYLCV), a small virus with a single-stranded DNA genome, underwent population bottlenecking in cells of its host plants. We engineered a TYLCV genome to produce two replicons that express green fluorescent protein and mCherry, respectively, in a replication-dependent manner. We found that among the cells entered by both replicons, less than 65% replicated both, whereas at least 35% replicated either of them alone. Further probability computation concluded that replication in an average cell was unlikely to have been initiated with more than three replicon genome copies. Furthermore, sequential inoculations unveiled strong mutual exclusions of these two replicons at the intracellular level. In conclusion, the intracellular population of the small DNA virus TYLCV is actively bottlenecked, and such bottlenecking may be a virus-encoded, evolutionarily conserved trait that assures timely selection of new mutations emerging through error-prone replication. Author summary: An important unresolved issue in virus life cycles is how natural selection acts on individual virus copies occupying the same cell. Unlike cellular organisms in which a chromosome harboring an advantageous or deleterious mutation usually shares the hosting cells with no more than one homologous sister chromosome, viruses could potentially reproduce with numerous genome copies per cell, permitting sharing of protein products, thereby greatly diminishing phenotypic impacts of otherwise eventful mutations. Previous investigations suggest that (+) RNA viruses solve this problem by bottlenecking the number of replicating genome copies in each cell to low single digits. The current study reveals strikingly similar intracellular population bottlenecks for a small DNA virus. Further mechanistic interrogations could avail the virus-encoded bottleneck-enforcing apparatus as targets for antiviral therapy and prevention. [ABSTRACT FROM AUTHOR]

High and stable rice yields are critical to global food security, and potassium-deficient soils in East Asia have seriously limited rice production in the regions. It is feasible to screen potassium efficient quantitative trait locus(QTLs) from existing rice varieties to cope with rice production in potassium-deficient areas, and the selection of population parents is the key to locating major QTLs. After a long period of natural selection, potassium efficient rice varieties mainly exist in the region where the soil potassium level is low. The present study chose the representative twelve high-yielding rice varieties in east Asia, firstly, to measure plant height, fresh sheath weight, and fresh blade weight under hydroponic conditions. Based on the difference and consistency of the three parameters, NP as low potassium tolerant, and 9311 as low potassium sensitive rice variety were screened. We further analyzed the relative values of the six parameters of NP and 9311 treated with a culture medium containing different potassium (K+) concentrations and showed that the two varieties significantly differed in multiple low potassium concentrations. Meanwhile, we calculated the coefficient of variation of twelve rice varieties and most of those parameters reached a maximum at 4 mg/L K+, indicating that this concentration was suitable for screening potassium-efficient rice. We also measured the potassium content and the potassium-related traits in NP and 9311 tissues, and found that NP and 9311 significantly differed in potassium translocation. These differences may be responsible for the long-distance transport of potassium from the root to the aboveground part. In conclusion, we identified a pair of parents with significant differences in potassium translocation, which can be used to locate the relevant QTLs with high potassium efficiency to cope with the crisis of soil potassium deficiency in East Asia. [ABSTRACT FROM AUTHOR]

COVID-19 was a harsh reminder that diseases are an aspect of human existence and mortality. It was also a live experiment in the formation and alteration of disease-related attitudes. Not only are these attitudes relevant to an individual's self-protective behavior, but they also seem to be associated with social and political attitudes more broadly. One of these attitudes is Social Darwinism, which holds that a pandemic benefits society by enabling nature "to weed out the weak". In two countries (N = 300, N = 533), we introduce and provide evidence for the reliability, validity, and usefulness of the Disease-Related Social Darwinism (DRSD) Short Scale measuring this concept. Results indicate that DRSD is meaningful related to other central political attitudes like Social Dominance Orientation, Authoritarianism and neoliberalism. Importantly, the scale significantly predicted people's protective behavior during the Pandemic over and above general social Darwinism. Moreover, it significantly predicted conservative attitudes, even after controlling for Social Dominance Orientation. [ABSTRACT FROM AUTHOR]

Pleiotropy—when a single gene controls two or more seemingly unrelated traits—has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56–32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low. Author summary: The genetic basis of complex traits has been thought to exhibit pleiotropy, which is the notion that a single locus can control two or more unrelated traits. Widespread reports in the human disease literature show genomic signatures of pleiotropic loci across many traits. However, little is known about the prevalence and behavior of pleiotropy in maize across a large number of phenotypes. Using association mapping of common alleles in over one hundred thousand traits, we determine how pleiotropic each region is and use these quantitative scores to functionally characterize each region of the genome. Our results show little evidence that pleiotropy is a common phenomenon in maize. We observe that maize does not exhibit the same pleiotropic characteristics as human diseases in terms of prevalence, gene expression, chromatin accessibility, or sequence conservation. Rather than pervasive pleiotropy, we hypothesize that strong selection on large and wide effect loci and the need for trait independence at the gene level keep the prevalence of pleiotropy low, thus, allowing for the adaptation of maize varieties to novel environments and conditions. [ABSTRACT FROM AUTHOR]

Studies on the molecular characteristics of chloroplast genome are generally important for clarifying the evolutionary processes of plant species. The base composition, the effective number of codons, the relative synonymous codon usage, the codon bias index, and their correlation coefficients of a total of 41 genes in 21 chloroplast genomes of the genus Arachis were investigated to further perform the correspondence and clustering analyses, revealing significantly higher variations in genomes of wild species than those of the cultivated taxa. The codon usage patterns of all 41 genes in the genus Arachis were AT-rich, suggesting that the natural selection was the main factor affecting the evolutionary history of these genomes. Five genes (i.e., ndhC, petD, atpF, rpl14, and rps11) and five genes (i.e., atpE, psbD, psaB, ycf2, and rps12) showed higher and lower base usage divergences, respectively. This study provided novel insights into our understanding of the molecular evolution of chloroplast genomes in the genus Arachis. [ABSTRACT FROM AUTHOR]

RNA viruses can exchange genetic material during coinfection, an interaction that creates novel strains with implications for viral evolution and public health. Influenza A viral genetic exchange can occur when genome segments from distinct strains reassort in coinfected cells. Predicting potential genomic reassortment between influenza strains has been a long-standing goal. Experimental coinfection studies have shed light on factors that limit or promote reassortment. However, determining the reassortment potential between diverse Influenza A strains has remained elusive. To address this challenge, we developed a high throughput genotyping approach to quantify reassortment among a diverse panel of human influenza virus strains encompassing two pandemics (swine and avian origin), three specific epidemics, and both circulating human subtypes A/H1N1 and A/H3N2. We found that reassortment frequency (the proportion of reassortants generated) is an emergent property of specific pairs of strains where strain identity is a predictor of reassortment frequency. We detect little evidence that antigenic subtype drives reassortment as intersubtype (H1N1xH3N2) and intrasubtype reassortment frequencies were, on average, similar. Instead, our data suggest that certain strains bias the reassortment frequency up or down, independently of the coinfecting partner. We observe that viral productivity is also an emergent property of coinfections, but uncorrelated to reassortment frequency; thus viral productivity is a separate factor affecting the total number of reassortants produced. Assortment of individual segments among progeny and pairwise segment combinations within progeny generally favored homologous combinations. These outcomes were not related to strain similarity or shared subtype but reassortment frequency was closely correlated to the proportion of both unique genotypes and of progeny with heterologous pairwise segment combinations. We provide experimental evidence that viral genetic exchange is potentially an individual social trait subject to natural selection, which implies the propensity for reassortment is not evenly shared among strains. This study highlights the need for research incorporating diverse strains to discover the traits that shift the reassortment potential to realize the goal of predicting influenza virus evolution resulting from segment exchange. Author summary: Influenza A viruses are global pathogens that cause significant human morbidity and mortality through seasonal epidemics and pandemics that arise every few decades. These viruses have remained a public health threat in large part due to their high genetic variability, notably through exchange of their eight genome segments when two viruses coinfect a cell. Thus, determining the potential for genetic exchange has been an urgent goal, but has been difficult to achieve. Here we developed a novel method to quantify genetic exchange in a diverse collection of human influenza A viruses. We show that patterns of genetic exchange show strong strain-dependence, and do not necessarily track similarity between coinfecting strains or their subtype (H1N1 or H3N2). Our data support that specific strains can consistently bias genetic exchange, increasing or decreasing exchange independent of the other coinfecting strain. Our study suggests the propensity for reassortment is not evenly shared among strains and that identifying high-reassortment strains could improve pandemic preparedness or reveal new treatments. [ABSTRACT FROM AUTHOR]

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason–a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar. Author summary: One of the most striking observations from the Genome Wide Association Studies (GWAS) is that the density of GWAS hits is much lower on X-chromosome compared to autosomes. This was initially explained by technical/analytical reasons such as lower coverage and lack of adequate methods to analyze X-chromosomal SNPs. Since then, a better coverage and better analytical methods to analyze X-chromosomal SNPs were developed. We recently revisited the issue and found that the density of GWAS hits on X-chromosome is at least 5-fold lower compared to autosomes. We demonstrated that the difference cannot be explained by technical or analytical reasons. We proposed a hypothesis of a real biological phenomenon underlying X versus autosomal differences in the density of GWAS-detected SNPs, namely that X-chromosome has a lower density of functional polymorphisms compared to autosomes because of a stronger selection against X-chromosomal mutations since X-chromosomal variants are more exposed to natural selection due to hemizygosity in males and X-chromosome inactivation in females. The hypothesis is supported by the analysis of the densities of intergenic, intronic and exonic SNPs on human chromosomes. [ABSTRACT FROM AUTHOR]

Two decades of in vivo imaging have revealed how diverse T-cell motion patterns can be. Such recordings have sparked the notion of search "strategies": T cells may have evolved ways to search for antigen efficiently depending on the task at hand. Mathematical models have indeed confirmed that several observed T-cell migration patterns resemble a theoretical optimum; for example, frequent turning, stop-and-go motion, or alternating short and long motile runs have all been interpreted as deliberately tuned behaviours, optimising the cell's chance of finding antigen. But the same behaviours could also arise simply because T cells cannot follow a straight, regular path through the tight spaces they navigate. Even if T cells do follow a theoretically optimal pattern, the question remains: which parts of that pattern have truly been evolved for search, and which merely reflect constraints from the cell's migration machinery and surroundings? We here employ an approach from the field of evolutionary biology to examine how cells might evolve search strategies under realistic constraints. Using a cellular Potts model (CPM), where motion arises from intracellular dynamics interacting with cell shape and a constraining environment, we simulate evolutionary optimization of a simple task: explore as much area as possible. We find that our simulated cells indeed evolve their motility patterns. But the evolved behaviors are not shaped solely by what is functionally optimal; importantly, they also reflect mechanistic constraints. Cells in our model evolve several motility characteristics previously attributed to search optimisation—even though these features are not beneficial for the task given here. Our results stress that search patterns may evolve for other reasons than being "optimal". In part, they may be the inevitable side effects of interactions between cell shape, intracellular dynamics, and the diverse environments T cells face in vivo. Author summary: T cells are immune cells with an astounding ability to move nearly anywhere in the body. This motion helps them detect and clear up pathogens, and understanding it is key to understanding T-cell immunity. Importantly, the continuous search for pathogens means that T cells face different challenges throughout their lifetime: their needle-in-a-haystack quest for the first signs of disease in lymph nodes differs greatly from their motion in an infected lung, or from how they patrol the skin to guard against future reinfections. These observations have raised the intriguing question: have years of evolution equipped T cells with distinct search "strategies", optimized for whichever searching tasks they might encounter? Although several studies have addressed this question in mathematical models, to date, none have explicitly considered the evolutionary process itself. Here, we directly simulate evolutionary optimization of T-cell search. We find that explicitly simulating "survival of the fittest searchers" can shed new light on why T cells move the way they do. Importantly, we find that the evolving movement patterns are only in part optimized "strategies"—while other parts may merely be "side effects" stemming from the constraints arising from the cell's molecular motor acting in a maze-like environment. [ABSTRACT FROM AUTHOR]

Complement Receptor Type 1 (CR1) is a malaria-associated gene that encodes a transmembrane receptor of erythrocytes and is crucial for malaria parasite invasion. The expression of CR1 contributes to the rosetting of erythrocytes in the brain bloodstream, causing cerebral malaria, the most severe form of the disease. Here, we study the history of adaptation against malaria by analyzing selection signals in the CR1 gene. We used whole-genome sequencing datasets of 907 healthy individuals from malaria-endemic and non-endemic populations. We detected robust positive selection in populations from the hyperendemic regions of East India and Papua New Guinea. Importantly, we identified a new adaptive variant, rs12034598, which is associated with a slower rate of erythrocyte sedimentation and is linked with a variant associated with low levels of CR1 expression. The combination of the variants likely drives natural selection. In addition, we identified a variant rs3886100 under positive selection in West Africans, which is also related to a low level of CR1 expression in the brain. Our study shows the fine-resolution history of positive selection in the CR1 gene and suggests a population-specific history of CR1 adaptation to malaria. Notably, our novel approach using population genomic analyses allows the identification of protective variants that reduce the risk of malaria infection without the need for patient samples or malaria individual medical records. Our findings contribute to understanding of human adaptation against cerebral malaria. [ABSTRACT FROM AUTHOR]

To date, ancient genome analyses have been largely confined to the study of single nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by typical low genome coverage (<1×) and short fragments (<80 bps), precluding standard CNV detection software to be effectively applied to ancient genomes. Here we present CONGA, tailored for genotyping CNVs at low coverage. Simulations and down-sampling experiments suggest that CONGA can genotype deletions >1 kbps with F-scores >0.75 at ≥1×, and distinguish between heterozygous and homozygous states. We used CONGA to genotype 10,002 outgroup-ascertained deletions across a heterogenous set of 71 ancient human genomes spanning the last 50,000 years, produced using variable experimental protocols. A fraction of these (21/71) display divergent deletion profiles unrelated to their population origin, but attributable to technical factors such as coverage and read length. The majority of the sample (50/71), despite originating from nine different laboratories and having coverages ranging from 0.44×-26× (median 4×) and average read lengths 52-121 bps (median 69), exhibit coherent deletion frequencies. Across these 50 genomes, inter-individual genetic diversity measured using SNPs and CONGA-genotyped deletions are highly correlated. CONGA-genotyped deletions also display purifying selection signatures, as expected. CONGA thus paves the way for systematic CNV analyses in ancient genomes, despite the technical challenges posed by low and variable genome coverage. Author summary: In parallel with developments in genomic technologies over the last decades, ancient genomics opened a new era in understanding the evolutionary history of populations and species. However, the field still needs novel computational methods for accurate and effective use of ancient genome data, which is mostly low-coverage and more challenging to analyse than modern-day genomes. Single nucleotide polymorphisms (SNPs), to date, have yet been the main source of information analysed in ancient genome studies. This is despite copy number variants (CNVs) harboring at least as much information as SNPs, especially with respect to natural selection. Here we developed CONGA, an algorithm for genotyping deletions and duplications in low-coverage genomes. We assessed its accuracy using simulations (with ancient-like data), and also studied its performance among 71 real ancient human genomes from different laboratories. We found that the common practice of authors filtering their ancient genome data before publishing prevents the reliable identification of duplications. Meanwhile, large (>1,000 base-pair) deletions can be detected even at quite low coverage (e.g. 0.5×). Deletions called in ancient genomes reflect population history and also show signs of negative selection. [ABSTRACT FROM AUTHOR]

Cancer progression is associated with the evolutionary accumulation of genetic mutations that are biologically significant. Mutations of the androgen receptor (AR) are associated with the development of prostate cancer (PCa) by responding to non-androgenic hormones, and the lack of annotations in their responsiveness to hormone ligands remains a daunting challenge. Here, we have used a yeast reporter system to quickly evaluate the responsiveness of all fifty clinical AR mutations to a variety of steroidal ligands including dihydrotestosterone (DHT), 17β-estradiol (E2), progesterone (PROG), and cyproterone acetate (CPA). Based on an AR-driven reporter that synthesizes histidine, a basic amino acid required for yeast survival and propagation, the yeast reporter system enabling clonal selection was further empowered by combining with a random DNA mutagenesis library to simulate the natural evolution of AR gene under the selective pressures of steroidal ligands. In a time-frame of 1–2 weeks, 19 AR mutants were identified, in which 11 AR mutants were validated for activation by tested steroidal compounds. The high efficiency of our artificial evolution strategy was further evidenced by a sequential selection that enabled the discovery of multipoint AR mutations and evolution directions under the pressure of steroidal ligands. In summary, our designer yeast is a portable reporter module that can be readily adapted to streamline high-throughput AR-compound screening, used as a PCa clinical reference, and combined with additional bioassay systems to further extend its potential. Author summary: Androgens are a group of steroid-type sex hormones that regulate secondary sexual characteristics through interacting with the androgen receptor. Androgen-activated androgen receptor has been linked to prostate cancer progression, thus steroid drugs structurally similar to but different from androgens are clinically used to block androgen activation and to treat prostate cancers. However, mutations of the androgen receptor occur in clinics. As a result of some of these mutants, the steroidal hormone drugs become inactive and even by themselves can activate the androgen receptor exacerbating the disease conditions. Due to lack of an efficient screening method, how other clinical mutants respond to various steroidal hormone drugs remain unknown. Here, we developed a yeast-based system that allows high throughput study of the interaction between each hormone drug and androgen receptor mutant by eye-ball observation of yeast colony formation. Using this method, we thoroughly investigated all 50 clinically relevant mutants of the androgen receptor and how they individually respond to each of a variety of hormone drugs. Furthermore, the capacity of this system allowed us to perform random mutagenesis and quickly accumulate high volume of data regarding a large number of mutants, informative in guiding future precision medicine to treat prostate cancer. [ABSTRACT FROM AUTHOR]