BMC Evolutionary Biology

Hearing in Lepidoptera mediates a range of ecologically important behaviours, including mate communication, predator avoidance, and acoustic signalling. In moths, the evolution of predator-prey interactions with bats has further shaped hearing through a sensory arms race, with repeated co-option of auditory organs to detect and evade echolocating predators. Despite significant prior characterization of the neurophysiology and behaviour of hearing in moths, the genetic basis of hearing is poorly understood in most insects. In this study, we identify a core set of putative auditory genes in Lepidoptera using a combination of homology-based searches from Drosophila and evolutionary rate analyses. We find 56 genes present across all species and investigate whether gene copy number varies among non-hearing and hearing lineages and among 3 different ear types. We discovered seven genes associated with ear type and one with ear presence, but did not find significant losses in gene copy number in non-hearing species. We identified three genes (btv, Dnai2, and nompB) with strong evidence of selection in hearing clades and five genes with weaker evidence of selection. We discuss the potential roles of btv, nompB, and Dnai2 in ciliary transport and the aging of hair cells, as well as the possibility of actively amplified hearing. Our study serves as a primer and resource for further gene mining and functional testing of auditory genes in moths and other insects.

Protein evolution is fundamentally shaped by epistasis, where the effect of a mutation depends on the sequence context. As standard phylogenetic methods assume independently evolving sites, there is a need for more complex models based on accurate estimations of the fitness landscape. Good candidates are modern generative models -- such as the Potts model -- which successfully capture epistatic effects. However, recent work on generative evolutionary models usually use discrete time, making them difficult to integrate with the standard frameworks in evolutionary biology. We introduce a continuous-time sequence evolution model using the Gillespie algorithm and parameterized by a generative Potts model. This approach enables us to simulate realistic, family-specific evolutionary trajectories and allows for direct comparison with independent-site models. Surprisingly, we find that while epistasis significantly slows down evolution, it does not change the average evolutionary rates at individual sites. This is explained by the rate heterogeneity caused by context-dependence: we show that the rate at some positions varies between null to high values depending on the context, while other positions are essentially independent from the context. Finally, we show that epistasis leads to a systematic underestimation bias in the inference of evolutionary distance between sequences. Overall, our work provides a new tool for simulating realistic protein evolution and offers novel insights into the complex interplay between epistasis and evolutionary dynamics.

In most organisms, all cells inherit the same genome, and many mechanisms exist to preserve its integrity across cell divisions. Programmed DNA elimination (PDE), the targeted removal of specific genomic regions from somatic cell lineages during early embryogenesis, is a striking exception. Since its discovery in parasitic nematodes over a century ago, PDE has been observed in diverse eukaryotes, including ciliates, arthropods, and vertebrates. However, the mechanisms, functions, and evolutionary origins of PDE remain poorly understood. Here, we describe the discovery of PDE in three species of the free-living nematode genus Caenorhabditis. Multiple genomic regions are precisely eliminated from somatic cells during early embryogenesis, resulting in chromosome fragmentation and the loss of key germline genes. The sites of elimination are strongly associated with conserved sequence motifs that likely direct DNA breakage. Comparative analyses indicate that PDE was present in the last common ancestor of Caenorhabditis and subsequently lost early during the evolution of many species, including C. elegans. The presence of PDE in the ancestors of one of biology's most important model organisms, together with recent discoveries in other eukaryotic lineages, reveals PDE to be a far more widespread and significant feature of evolution and development than previously recognised.

Mutational biases can influence genome composition, but their contribution to protein evolution remains difficult to quantify. Here we utilize a nearly neutral framework that translates nucleotide mutational spectra into expected amino acid substitution patterns and equilibrium amino acid compositions. Using SARS-CoV-2 as a model system, we show that the viral mutational spectrum explains more than 50% of the variation in observed single-nucleotide amino acid substitutions and predicts the overall direction of proteome-wide amino acid composition change during the COVID-19 pandemic. The predictive power of the model varies with selection regime: effectively neutral and weakly deleterious substitutions conform most closely to the mutational expectation, whereas strongly constrained sites and mutational hotspots show larger deviations. This indicates that departures from the nearly neutral baseline provide a quantitative proxy for purifying and positive selection. Extending the analysis across 34 RNA virus species, we find that positive-sense, negative-sense and double-stranded RNA viruses differ systematically in their mutational spectra, and that these differences are associated with predictable shifts in proteome composition. The same relationship is detectable in RNA-dependent RNA polymerase sequences from more than 77,000 viral species. These results indicate that taxon-specific mutational bias contributes persistently to protein evolution across evolutionary scales.

Ancient environmental DNA (aeDNA) from permafrost, lake, cave, and marine sediments provides a rich source of genetic data that captures broad perspectives of past biodiversity. Accurate dating is crucial for discovering ecologically relevant patterns from aeDNA, and molecular clock dating would allow for sample ages to be estimated from the recovered genetic material itself instead of the geological components. However, the fragmented and damaged nature of short-read ancient DNA (aDNA) from multiple taxonomic sources poses significant challenges and has limited this dating approach for aeDNA. Here we developed ratePlacer, a phylogeny-based method for analyzing aeDNA that can combine information from many short reads in a sample while accounting for DNA damage to provide maximum likelihood estimates of sample ages. Simulations demonstrate that ratePlacer accurately dates samples even under the fragmented, damaged conditions characteristic of aeDNA and outperforms Bayesian tip-dating approaches for taxonomically mixed samples commonly found in aeDNA. Yet age estimates from re-dating Kap Kobenhavn varied across taxa, highlighting the difficulty of molecular clock dating in aeDNA. This dating also revealed elevated G[->]T and C[->]A mismatches consistent with oxidative damage. These patterns reveal aDNA damage beyond deamination and that remains understudied, suggesting that aeDNA should be carefully evaluated in genomic and evolutionary analyses. The new dating method, ratePlacer, extends molecular clock dating of aDNA from single-specimen to pooled environmental DNA data, where traditional methods struggle.

Collembola (springtails) are among the most abundant and ecologically important soil arthropods, representing one of the oldest extant terrestrial hexapod lineages, with a fossil record extending to the early Devonian. Despite their relevance, phylogenetic relationships among the four extant orders (Entomobryomorpha, Poduromorpha, Symphypleona, and Neelipleona) have remained unresolved for over two decades. Here, we present the most comprehensive phylogenomic analysis of Collembola to date, comprising 1,127 single-copy orthologues from 145 taxa representing 19 families. To improve orthology inference, we developed a novel HMM-based filtering pipeline that significantly reduced hidden paralogy in BUSCO-derived datasets. Across multiple dataset configurations, gene-jackknife replicates, and various maximum-likelihood analyses, we consistently recovered Poduromorpha as the earliest-diverging lineage. Coalescent-based methods instead highlighted discordant arrangements characterised by extremely short internal branches and low quartet support, a pattern consistent with pervasive incomplete lineage sorting and reticulate evolutionary history. We further dissected the phylogenetic signal by exhaustively evaluating all possible inter-order topological arrangements, both on the full concatenated dataset and gene-by-gene, to identify the most phylogenetically informative loci. These analyses rejected the great majority of previously proposed hypotheses, narrowing support to only two statistically indistinguishable topologies (T11 and T4), with the Poduromorpha-first arrangement consistently favoured across both site-homogeneous and site-heterogeneous substitution models. Finally, with molecular dating, we estimated the origin of crown Collembola in the Early Devonian, with the diversification of the extant orders in the Carboniferous. Several extant genera were estimated to be older than many currently recognized families, highlighting the exceptional evolutionary persistence of springtail lineages and suggesting that lineage longevity should be considered when interpreting higher-level taxonomic diversity.

The deepest ocean is one of the most extreme environments for life on our planet, combining near-freezing temperatures, low oxygen levels, and hydrostatic pressures reaching 111 MPa (1100 atm). Extreme pressures are predicted to alter many aspects of biology, including the physical properties of biological hydrogels, protein structure, and the solubility of gases in water. How organisms have adapted to live in these conditions is poorly understood. Studying these organisms in situ is difficult and requires specialized deep-sea equipment capable of withstanding the extreme pressure; raising these organisms in captivity is also challenging due to their extreme habitat requirements. Given these difficulties in studying deep-sea organisms, we set out to identify the problems shallow-dwelling organisms face due to increased pressure. These can provide insights into how organisms tolerate life in the deepest parts of the ocean. This project aims to take embryos of the shallow-dwelling aquatic organism Xenopus laevis, determine how surface-dwelling organisms fail under high hydrostatic pressure, and identify a means to survive this deadly pressure. We have designed a system to expose different embryonic stages of X. laevis to high pressures and observe its effects. After identifying the limits of survivability, we sought to understand how these embryos can acclimate to changing pressures. Comparative RNA-seq and cross-species analyses revealed a conserved, pressure-induced transcriptional response across phyla, with the heat shock pathway among the most strongly activated. Pre-activation of this pathway via prior pressure or other stressors enhances survival under otherwise lethal hydrostatic conditions.

Protein language models (PLMs) score the effects of amino acid replacements as pseudo-probabilities, which are widely utilised to map protein fitness landscapes. However, because their training data relies on natural amino acid sequences, these models conflate protein structural constraints with nucleotide mutation biases and codon accessibility. Using the rapid emergence of the divergent influenza A H3N2 K lineage as a stress test, we investigate how base PLMs (ESM-2 and ESM-C) versus fine-tuned versions of these models capture mutational processes. We systematically implement a parameter sweep to explicitly couple (or decouple) empirical nucleotide mutational supply from PLM-assessed amino acid substitution pseudo-probabilities across evolutionary forecasting tasks. We find that base PLMs implicitly learn generic nucleotide-level mutational constraints, an effect strongly amplified by virus-specific fine-tuning. Incorporating explicit mutational accessibility significantly improves the binary prediction of observed amino acid changes. Conversely, when predicting the final circulating frequency of variants that have already emerged, adding mutational supply degrades performance, confirming that selection dominates post-emergence dynamics. Additionally, we perform amino-acid-level epistatic scanning to investigate protein structural constraints in the context of genetic background. This indicates the improbable antigenic substitution I160K is dependent on co-occurring S144N and N158D mutations in the H3N2 K lineage. Ultimately, current PLM pseudo-probabilities are a composite metric that conflates protein structural fitness with historical biases in mutational supply. Explicitly decoupling these independent evolutionary processes optimises predictive accuracy for real-world pathogen forecasting and isolates pure protein fitness for synthetic design pipelines.

A fundamental unresolved question in molecular evolution is how novel genes arise from noncoding DNA and become fixed within stable gene repertoires. Here, we performed comparative genomic analyses across evolutionary timescales in insects using chromosome-scale genome assemblies of two cricket species, Teleogryllus occipitalis and Tarbinskiellus portentosus. Using conservative criteria, we identified 41 de novo gene candidates derived from intergenic regions in the Te. occipitalis lineage. These genes are simple and compact, exhibit hallmarks of evolutionarily young genes, and frequently contain fragments of transposable elements and simple sequence repeats. Across insects, such repetitive sequence fragments show positional homology but lack sequence conservation in older genes, suggesting that they serve as sequence material for gene emergence during early stages of gene evolution. In contrast, insertions after gene establishment are strongly constrained. We propose a model in which stages of gene evolution are characterized by shifts in selective pressure on the incorporation of sequence material.

Introgression following hybridization is increasingly recognized as a major driver of evolution. However, its importance depends on its frequency in nature, which remains to be quantified. To address this, we provide a snapshot of ongoing introgression in a whole species assemblage (4126 ant colonies). 23% of all 82 local species show signs of introgression, which is more than twice previous estimates. Introgression is typically subtle, yet we find that it contributes measurably to genetic diversity. Species divergence, rather than classical prezygotic reproductive barriers (mating phenology, ecological niche, fine-scale habitat use) constrains introgression, suggesting that the main reproductive barriers are postzygotic at this stage of divergence. Our results indicate that introgression may be a common but often overlooked feature of natural communities.

Aim: To forecast near-future arrivals of non-native terrestrial and freshwater vertebrates at the regional level. Location: Global (geopolitical regions worldwide, including countries and main administrative divisions). Methods: We compiled first regional record data and assembled functional and macroecological variables for 1,931 non-native vertebrate species. For each region, we identified recently arrived non-native species using retrospective windows of thirty and twenty years ending in 2015 (1986-2015; 1996-2015). We then fitted region-specific random-forest models classifying recently arrived species versus those not yet arrived using as predictors: (i) harmonised species traits (e.g., habitat, diet, body size and native-range attributes) and (ii) spread history, capturing time since first record elsewhere. Predictive performance was evaluated using leave-one-out cross-validation, comparing full models with trait-only and spread-only variants. We also assessed relationships between predictive accuracy, predictor importance, and the geographic positioning and trade connectedness of regions. Finally, we predicted region-specific probabilities of arrival for species not yet recorded. Results: Forecasting accuracy was consistently high across regions and taxa, with AUC values above 0.9 in more than half of the focal regions. Full models substantially outperformed models using either predictor set alone, and spread-history-only models typically exceeded trait-only models. Relative importance of spread-history predictors declined with geographic distance to the focal region, whereas predictability was lower in highly trade-connected regions. Predicted near-future high-risk arrivals were dominated by birds and freshwater fishes and showed strong regional structuring. A small set of species ranked highly across many regions (e.g., birds: Phasianus colchicus, Acridotheres tristis, Amandava amandava, Colinus virginianus, Corvus splendens and Lonchura malacca; fishes: Coregonus peled and Oreochromis mossambicus; mammal: Oryctolagus cuniculus), suggesting substantial unrealised spread potential. Main conclusions: Near-future regional arrivals of non-native vertebrates are predictable from spread history and species traits. This enables scalable, updateable regional watchlists to support prevention, early detection and horizon scanning.

The global invasion of the Asian tiger mosquito Aedes albopictus has led to an increase in arboviral disease, including within Mesoamerica. Understanding vector invasion routes is important for public health because it directs biosecurity and identifies sources of adaptive allele spread. Panama is an important hub of global trade with opportunities for Aedes introduction through both maritime and overland routes but dispersal into the Isthmus has not yet been investigated. We therefore sought to investigate the population structure and invasion history of Ae. albopictus into Panama, targeting both its mitogenome and associated Wolbachia. Historical demographic analysis with Bayesian phylogeographic diffusion models and estimates of divergence revealed that Panamanian Ae. albopictus and its associated Wolbachia have a convergent evolutionary history resulting from multiple introductions. Both could be traced to Asian-derived lineages introduced via the Americas, with invasion primarily through the maritime trade of the Panama Canal rather than overland dispersal from neighboring Costa Rica. An investigation of the relative density of Wolbachia in Panama revealed that both the strains wAlbB and wAlbA were at a notably lower density compared to other worldwide locations. This finding has implications for arbovirus transmission and raises important questions about how Wolbachia density is impacted by the environment and impacts on population control. Overall, the Panama Canal is a key route for vector introductions into Mesoamerica.

Blood group antigens, defined by epitopes on the erythrocyte surface, are central to transfusion safety and maternal-fetal compatibility. While the genetic basis of many clinically relevant blood group antigens is well established, which structural and biophysical parameters determine whether a single-nucleotide variant gives rise to an antigenic phenotype remains unclear. Here, we integrate structural, biophysical, and evolutionary analyses to systematically evaluate features associated with single amino acid substitutions across 24 human protein-based blood group systems. We analyse 319 variants with curated phenotypic annotations alongside 481 control variants, identifying key determinants of null and antigenic phenotypes. Null variants are characterized by high evolutionary conservation, burial within the protein core, loss of hydrophobicity, increased polarity, and a propensity for arginine substitutions. Antigenic variants are also enriched in arginine; however, in contrast to null variants, they tend to occur at less conserved, more solvent-accessible, and structurally flexible sites. Supervised machine learning models trained on structural and biophysical descriptors were applied to distinguish (i) null and (ii) antigenic variants from controls, achieving balanced accuracies of 0.82 and 0.63, respectively. Feature importance analysis identified predicted pathogenicity, solvent accessibility, and evolutionary conservation as the most predictive determinants of null variants, whereas hydrophobicity, conservation, and flexibility dominated antigen prediction. This work establishes a framework linking molecular variation to blood group phenotypes and provides a foundation for predicting the impact of novel missense mutations in transfusion medicine and beyond.

The Patterson's D statistic detects gene flow from ABBA-BABA site patterns, but its biallelic site patterns fail under deeper divergences where multiple hits cause false positives. We propose two extensions, D+ and D*. Both incorporate multiallelic site patterns to reduce saturation bias under JC and F84 model. Simulations show that D+ and D* both remain correctly null under all conditions and detect gene flow effectively, with distinct advantages: D+ guarantees non-negativity of the denominator, while D* provides greater robustness when mutation rates vary across genomic regions. The source code and binary files are publicly available at https://github.com/chaoszhang/ASTER.

Conjugative plasmids drive the dissemination of antimicrobial resistance genes and other conditionally beneficial accessory genes, but otherwise inflict fitness costs on their hosts that limit their spread. These costs can be ameliorated by compensatory mutations on the chromosome, the plasmid, or both combined (combined compensation). Mutants with plasmid-borne or chromosomal compensation differ in how they spread and compete, making the location of compensation an important determinant of plasmid and antimicrobial resistance (AMR) dynamics. We collate experimental data on plasmid-host co-evolution which, albeit limited, suggest compensatory benefits differ by location and are highest for combined compensation. We develop and analyse a mathematical model of compensatory evolution, finding that the long-term location of compensation is mainly determined by the highest cost reduction. Short term, however, succession dynamics arise from differences between locations: for instance, plasmid-borne compensation spreads horizontally, initially dominates, and can even facilitate the establishment of chromosomal or combined compensation. Strong trade-offs between compensation and either resistance or conjugation render compensation non-viable, but only conjugation trade-offs are location-dependent, disadvantaging plasmid-borne compensation and generating oscillatory dynamics. Our findings suggest plasmid-borne compensation may initially accelerate AMR-plasmid spread, whereas long-term chromosomal or combined compensation may enable hosts to accumulate multiple AMR-plasmids, promoting multidrug resistance.

Animal vocalisations can convey information about external events, but whether this goes beyond reflecting the emotional state elicited by these events is debated. To explore this, we studied the acoustic structure of common marmoset (Callithrix jacchus) phee (long-distance contact) and ek (alert/mobbing) calls produced in five treatments varying in the emotional valence and arousal they elicit (internal state), as well as food and social context (external events). We measured changes in arousal via nasal temperature and analysed both basic acoustic parameters and Mel-frequency cepstral coefficients (MFCCs) of the calls. Support Vector Machines combined with Linear Mixed effect models revealed that phee calls encode both external events and internal states, while eks reflected predominantly arousal. Notably, an acoustic signature related to food context was present in phees both when provided (positive valence) and teased with highly preferred food items (negative valence), and even when food was not physically present (food call playback treatment). This suggests marmoset long-distant phee calls encode external information beyond emotional arousal and valence, and independently of the presence of an immediately triggering stimulus.

Animals balance staying in a favorable environment with exploring new ones. In C. elegans chemotaxis, the process by which worms migrate toward an attractant has been extensively studied. However, what happens after they reach it remains largely unexplored, partly because conventional assays immobilize worms at the point of arrival. Here, we quantitatively analyzed chemotactic behavior upon reaching an attractive odor source using an immobilization-free chemotaxis assay. We observed that 62% animals left the isoamyl alcohol region after initially approaching it, a behavior we termed "leaving behavior." Quantitative analysis revealed that leaving behavior represents a distinct locomotor state compared with free-moving, high-concentration odor avoidance, and approach behavior. To test whether leaving behavior is related to olfactory adaptation, we analyzed mutants in adaptation-related genes. The proportion of leaving behavior was significantly increased in egl-4 loss-of-function mutants compared with wild-type animals, whereas arr-1 mutants showed no significant difference. These results suggest that egl-4 negatively regulates leaving behavior, suggesting a role for this kinase in stabilizing post-arrival behavioral states beyond its known function in olfactory adaptation. Our findings indicate that chemotaxis involves dynamic behavioral transitions even after reaching an attractant, consistent with an exploration-exploitation trade-off framework.

Whole-genome alignment (WGA) is widely used for genome-scale phylogenetic inference, and most scalable WGA pipelines rely on progressive alignment guided by a pre-specified tree. Among progressive whole-genome aligners, Progressive Cactus is a successful state-of-the-art method. However, analyses of real and simulated avian data indicate that guide-tree choice can influence downstream tree inference; star guide trees do not remove this effect and can exacerbate long-branch attraction artefacts. We have developed a consensus strategy based on the Progressive Cactus framework by generating a small set of alternative guide-tree alignments and retaining only homology relationships consistently recovered across all alignments. In simulation experiments, consensus alignments improve precision, bring inferred site-pattern frequency distributions closer to those of the true alignments, and recover more true splits than single guide-tree alignments. In a real landbird (Telluraves) dataset, we observe a strong bias towards single binary guide trees and long-branch attraction for less resolved trees. While the reconstructed tree still depends on the phylogenetic method and taxa sampling, our consensus alignment has no clear bias. We implemented a hierarchical consensus workflow that only locally resolves uncertainty in the guide tree. Therefore, the computational cost increases only moderately, for example by an estimated 68 percent for a recently published large-scale alignment of more than 300 modern birds (Neoaves) taxa.

We present Nemo 2.4, an advanced forward-time individual-based simulation framework designed to model the complex eco-evolutionary dynamics and genetic basis of quantitative traits. This tool addresses current challenges in evolutionary quantitative genetics by providing unprecedented flexibility and computational efficiency. Nemo 2.4's modular architecture allows researchers to design custom life cycles by combining specialized Life Cycle Event (LCE) modules, from reproduction and dispersal to selection, crossing, and phenotype expression. The software supports diverse population models, including both Wright-Fisher (WF) and non-WF dynamics, spatially explicit models, and varying demography. Nemo 2.4 handles a wide range of genetic architectures, including both multi-allelic Quantitative Trait Loci (QTL) for general trait studies, and dense di-allelic Quantitative Trait Nucleotides (QTN) implemented with highly optimized bit-wise data structures. Crucially, it allows the simulation of QTNs on comprehensive genetic maps that incorporate other genetic elements, providing genomic-scale resolution. Key biological complexities are integrated natively: the model accommodates modular pleiotropy, dominance, and pairwise epistasis across multiple traits, facilitating the study of complex genotype-phenotype mappings. Furthermore, Nemo 2.4 models phenotypic plasticity through reaction norms and incorporates underlying liability thresholds, enabling the simulation of environmental influences on trait evolution with various forms of selection (e.g., Gaussian, linear, truncation). Due to its compiled design and memory-efficient data representations for large numbers of loci, Nemo provides a robust platform for running high-throughput simulations critical for testing theoretical predictions in polygenic adaptation and understanding evolutionary responses to changing environments.

Aedes albopictus is a major arboviral vector whose global expansion, driven by human activities and climate change, poses a growing public health concern for a number of neglected tropical diseases in both tropical and temperate regions. As a poikilotherm, its biology and population dynamics are strongly influenced by temperature, thereby shaping disease transmission. To thwart and control its geographic expansion, effective vector control strategies are increasingly critical. Densoviruses (DVs), such as AalDV2, are being explored as mosquito viral biocontrol agents due to their restricted host range and ability to disseminate through oviposition sites. However, the influence of environmental parameters on the interactions between Ae. albopictus and AalDV2 remains poorly understood. This makes their performance under realistic, fluctuating thermal regimes difficult to estimate. In this study, we investigated the combined effects of temperature and AalDV2 exposure on Ae. albopictus survival and development across its full life cycle. Mosquitoes were reared under fluctuating temperature regimes (26-28 {degrees}C and 32-34 {degrees}C, 12:12 day[ndash]night cycles) and exposed to AalDV2 or a control treatment. Chronic exposure to 32-34 {degrees}C significantly reduced overall survival, decreasing median lifespan by approximately 10 days (HR=2.21, p=0.0018), with a deleterious effect increasing over time. It extended aquatic lifespan and increased pupal mortality. It also reduced adult lifespan in both sexes with a stronger effect in females. AalDV2 exposure had no significant effect on overall survival, stage-specific mortality, or adult lifespan. However, a significant interaction between viral exposure and thermal stress was detected on aquatic lifespan: AalDV2-exposed females showed further extended larval and pupal development specifically under the 32-34 {degrees}C regime, without any effect on survival. These results indicate that the biocontrol potential of AalDV2 cannot be assessed independently of thermal context: while lethal effects were absent under both fluctuating regimes, the prolongation of aquatic development by the virus under thermal stress may have indirect consequences for mosquito population dynamics that warrant further investigation.