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The dependence of evolutionary rate estimates on the timeframe of sampling poses a fundamental challenge for reconstructing evolutionary histories from molecular sequence data, which is central to evolutionary biology and infectious disease research. We present a novel and flexible approach to accommodate time-varying evolutionary rates by modeling the sequence substitution process using inhomogeneous continuous-time Markov chains (ICTMCs) acting along the branches of the phylogeny, and parameterizing the log transformed rate as a smooth function of time using a cubic B-spline basis expansion. Following the parlance of phylogenetics that refers to rates of molecular substitutions as molecular clocks, we call this a spline clock model. Integrals of the rate function over all branches, required for likelihood evaluation, are approximated efficiently using Gauss-Legendre quadrature, and smoothness is enforced by assigning a Gaussian Markov random field prior to the spline coefficients. Through a simulation study, we demonstrate that the spline clock model recovers the true time-varying rates more accurately and with tighter credible intervals than competing clock models. We apply the spline clock model to examine the evolutionary rate of foamy virus and the rate of spatial diffusion of SARS-CoV-2 across Europe, recovering strong time-varying signal in both settings.

Unclear species boundaries are a common challenge in ecological studies and species inventories, obscuring patterns of biodiversity and complicating inferences about ecological and evolutionary processes. One possible cause of fuzziness is hybridisation, but little information exists on hybridisation in the tropics. Here we aim to address the question of potential hybridisation in the tropical American fern genus Trichomanes (Hymenophyllaceae) and to clarify evolutionary relationships within the genus. We take advantage of nuclear target-capture sequencing, haplotype phasing and off-target plastid reads to detect phylogenetic relationships and reticulate histories. We sampled 303 individuals that represent 41 known Trichomanes species. Our analyses recovered a robust backbone for the genus, strongly supporting subgenus Trichomanes as sister to subgen. Feea, and subgen. Davalliopsis as sister to subgen. Lacostea. Trichomanes pinnatum proved to be a complex where six different unnamed morphs represent four different kinds of evolutionary history. Two morphs appear to be stabilised, relatively old hybrid-derived species, one represents a recent hybrid, one reflects repeated spontaneous hybridisation between the same parent species, and one represents within-species morphological variation without any obvious genomic signal. Excluding the four hybrids makes T. pinnatum s.s. morphologically and genetically much more uniform, but its geographic distribution still covers practically all tropical America, indicating that the species may be approaching panmixia.

The human coagulation system has resulted from thousands of years of evolutionary pressure, finely balancing the competing risks of lethal hemorrhage and catastrophic thrombosis. There are several examples of the close interaction between the two opposing sides of hemostasis. A paradigmatic case is offered by the factor V Leiden mutation. Emerging approximately 22,000 years ago, this gain-of-function variant is now expressed in about 5% of Caucasians. While it now significantly elevates the risk of venous thromboembolism, it historically provided crucial evolutionary advantages, including reduced peripartum blood loss, enhanced iron stores, improved fertility, and increased embryo implantation rates. Conversely, congenital hypocoagulable states, such as hemophilia, demonstrate the other side of the coin. With modern therapeutic advances now extend the life expectancy of individuals with hemophilia, clinicians are increasingly managing age-related comorbidities. While experimental models suggest a chronic hypocoagulable state might mitigate the risk of occlusive cardiovascular events or suppress cancer metastasis, clinical and epidemiological data remain conflicting. Mild factor deficiencies may offer some protection against ischemic heart disease, but overall cardiovascular and oncological risk profiles in patients with hemophilia increasingly mirror those of the aging general population. This review explores the genetic and environmental forces that shape this homeostatic equilibrium, illustrating how historical survival mechanisms influence modern disease patterns.

Many forms of maternal effects are said to be 'cascading', in which an individual's phenotype is partially a function of its mother's phenotype. The mother's phenotype is also partially a function of the grandmother's phenotype, and so each individual phenotype depends on the phenotypes of all its previous maternal lineage ancestors. In previous work we developed quantitative genetics models to assess the evolutionary consequences of such cascading maternal effects under pragmatic modelling assumptions. Here we show that the theoretical framework underlying our previous studies should be extended to treat past maternal states as being under selection in the current generation to account more consistently for the cascading nature of phenotypic parental effects. While accommodating selection of past maternal states does not significantly change the qualitative results of our previous studies, we find typically small quantitative changes in the evolving genetic components, offspring phenotype and fitness. The extended framework offers a conceptually more consistent approach that can inform future quantitative genetics models of parental effects. In particular this new formulation captures the impact of genetic covariances between current and past states on the evolutionary dynamics, and provides a flexible framework that can be adapted to analyse transgenerational influences from any combination of ancestors.

Domestication is an evolutionary process guided by humans. It operates continuously in multiple directions, with species undergoing adaption to diverse human-influenced environments and cultural and technological contexts. Mesoamerica is among the world's main regions of domestication and the Agave genus is widely used and managed in this region, with at least eleven domesticated and semi-domesticated species. One species, Agave americana, comprises two subspecies and four varieties, the diversification of which is hypothetically related to human management. This study explores this hypothesis by analysing morphological variation and its relation to environmental and management settings. Ethnobotanical studies were conducted to document uses, management practices, and targets of human selection, together with patterns of morphological variation across different ecological and cultural contexts. Semi-structured interviews were carried out with agave managers throughout the species' distribution range in Mexico. Morphometric analyses were performed on populations occurring under contrasting environmental and management conditions. Multivariate and univariate statistical analyses were used to evaluate morphological variation in relation to geographic distribution, environmental conditions, and management regimes. A. americana is used to extract sap for preparing the fermented beverage called pulque. Its stems have been used as food since prehistoric times, when they were cooked in underground ovens. This cooked matter forms the basis of current fermentation and the production of distilled mescal. The fibre of some varieties has been used to make cords and textiles. Both subspecies are managed, but the subspecies protamericana clearly has wild populations. Morphometric studies confirm the presence of traits indicative of domestication in most varieties of the subspecies americana. Phenotypic variation within the Agave americana complex is associated with taxonomic identity, geographic distribution, and management intensity. The observed patterns are consistent with the effects of human selection, particularly in traits related to plant size and the reduction of defensive structures. However, further studies integrating common-garden experiments, population genetics, and phylogeographic analyses are required to clarify the evolutionary history and genetic basis of the observed variation.

The Encyclopedia of Domains (TED) provides domain annotations for proteins in the AlphaFold Protein Structure Database (AFDB) using a consensus of three state-of-the-art structure-based methods. We used these annotations to construct profile Hidden Markov models (HMMs), collectively forming the TED Library of HMMs (TEDLH). TEDLH enables sensitive sequence and profile searches, supporting systematic exploration of protein domain families and their evolutionary relationships. TEDLH links 934,186 domain HMMs to experimentally determined CATH-PDB structures through direct (primary) and transitive (secondary and tertiary) relationships. Fewer than half of TEDLH HMMs are directly linked to a CATH-PDB domain; the remaining models are connected through transitive relationships. These transitive links extend coverage into more divergent regions of sequence space and better represent CATH superfamily diversity.HMM-HMM comparisons within CATH superfamily 3.30.70.100 illustrate how transitive relationships expand sequence coverage. In this superfamily, 5,640 TEDLH HMMs are connected to 173 CATH-PDB representatives. Primary, secondary, and tertiary relationships progressively capture more divergent sequences (pairwise sequence identity <20%) that retain structural similarity (TM-score &#x2265;0.6) and a conserved two-layer &#x3b1;/&#x3b2; sandwich core fold.All-against-all HMM-HMM comparisons across TEDLH also reveal sequence similarities across the CATH hierarchy (cross-hits). At low query coverage (<50%), cross-hits are more frequent between CATH classes, architectures and topologies, whereas at higher coverage thresholds (&#x2265;70%) they predominantly occur between superfamilies. These cross-hits are not driven by superfamily size or sequence diversity and can provide guidance for CATH curation. As an example, analysis of cross-hits between superfamilies 2.170.130.30 and 3.10.20.30 reveals evolutionary relationships between these groups. TEDLH is compatible with HH-suite3 and is available from FigShare https://doi.org/10.6084/m9.figshare.28531754 for local use. Supplementary data are available at Bioinformatics online.

Bacteriophages can evolve rapidly. Mutation and recombination via horizontal gene transfer allow them to counter adaptive responses by microbial hosts. However, little is known about the genomic processes underlying phage evolution within an ecological context-especially within natural microbial communities. This is due in part to the difficulty in resolving aspects of phage ecology, such as host range. To better understand the interplay of phage ecology and evolution within natural microbial communities, we combined measures of phage host range in vivo with measures of genome evolution in order to infer the evolutionary pressures acting on phage genomes within individual honeybee worker microbiomes. We show that near-identical phage genomes, cooccurring across multiple honeybee colonies, exhibit large variation with respect to gene modules, despite retaining a highly similar core genome. Estimates of genic diversity suggest deviations from neutral evolutionary models and identify loci under putative diversifying selection. We then use HiC-resolved metagenomics and show that the honeybee gut contains a dense phage community that exhibits a wide degree of host range variation. This variation differed across individual metagenomes in both the number and phylogenetic distance of potential hosts. We show that common measures of genetic variation positively correlate with host range in bee-associated phages and that functional targets of diversifying selection are partitioned differently between broad or narrow host range phages. Our work underscores the high host range variation associated with phages within host-associated microbial communities and provides evidence that this variation impacts rates of phage evolution.

Research on regional circulation and evolution of influenza A viruses before and after the COVID-19 pandemic is crucial for informing vaccine updates and antiviral drug development. This study generated 260 new genomic sequences of influenza A(H1N1)pdm09 viruses collected in Yunnan province, China, between 2018 and 2023. Comparative genomics analyses elucidated their evolutionary characteristics and dynamics. Epidemiological analysis identified key risk factors (sex, age, occupation) for influenza infection. Phylogenetic analyses revealed the sequence divergences between the vaccine strains and Yunnan circulating strains, especially in the 2020-2024 influenza seasons. The subclade reassortment events were extremely limited among these sequenced Yunnan strains, suggesting the reassortment may be not a major contributor for the circulation and evolution of influenza A(H1N1)pdm09 viruses in Yunnan during these influenza seasons. We detected the elevated evolutionary pressures acting on the specific gene segments, reflected in increased dN/dS ratios, particularly for envelope proteins. Furthermore, numerous amino acid substitutions (e.g., S185I/T) within HA antigenic epitopes and receptor binding sites were identified in most Yunnan strains, indicating potential roles of antigenic drift in modulating viral antigenicity and host adaptation. Notably, 17 amino acid substitutions in HA and NA (including HA: N156K) accumulated to higher frequencies during the 2022-2023 and 2023-2024 seasons. These changes likely represented the molecular signature of contemporary A(H1N1)pdm09 viruses in Yunnan. Collectively, this study explored the molecular evolutionary dynamics of A(H1N1)pdm09 viruses in Yunnan province during diverse influenza seasons, providing new regional data for studying molecular characterization and evolution of A(H1N1)pdm09 within the global surveillance framework.

Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of their computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here, we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long time-scale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that can be transferred across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational ISM poised to accelerate the development of predictive large-scale simulation tools.

Rutaceae encompasses numerous medicinal plants with unclear genetic relationships and mitochondrial genome (mitogenome) characteristics, which hinders understanding of their evolutionary adaptation and medicinal trait development. Here, we aimed to investigate the conserved and divergent features of mitogenomes among closely related medicinal genera of Rutaceae, and to explore how mitogenomic variations correlate with their phylogenetic positions. Citrus aurantium, Toddalia asiatica, and Zanthoxylum nitidum (Roxb.) DC are medicinally valuable but understudied Rutaceae species belonging to different genera. We sequenced, assembled, and annotated their complete mitogenomes: C. aurantium (504,387&#xa0;bp, 45.17% GC content) contained 62 genes (36 protein-coding genes (PCGs), 3 rRNAs, and 23 tRNAs), T. asiatica (566,784&#xa0;bp, 45.37% GC content) harbored 58 genes (35 PCGs, 3 rRNAs, and 20 tRNAs), and Z. nitidum (539,013&#xa0;bp, 45.48% GC content) possessed 61 genes (34 PCGs, 3 rRNAs, and 24 tRNAs). Comparative analyses with 12 published Rutaceae mitogenomes showed that the three species exhibited the highest number of RNA editing sites in PCGs among the examined species. Evaluation of selective pressure and gene clusters revealed fewer differences between C. aurantium and C. maxima cultivar Hirado Buntan than with the other two species, while C. sinensis displayed greater dissimilarities with other Citrus species (notably, C. maxima and C. maxima cultivar Hirado Buntan were conspecific and should have the least divergence). Phylogenetic trees constructed using 24 conserved mitochondrial PCGs clarified evolutionary relationships: C. maxima was closest to C. sinensis, followed by C. maxima cultivar Hirado Buntan and C. aurantium formed an unexpected separate clade, highlighting the need for more Rutaceae species' mitogenome in future phylogenetic analyses. This study expands the resource pool in Rutaceae mitogenomic resources, provides insights into features that may be associated with ecological adaptation, and offers valuable genomic data for future phylogenetic and comparative studies of Rutaceae medicinal plants.

Predicting the pathogenic consequences of protein mutations is a cornerstone of precision medicine, yet it remains a formidable challenge for transmembrane proteins (TMPs), a clinically vital class of drug targets. Existing computational methods are often hampered by their reliance on evolutionary data and fail to model TMP-specific biophysical constraints. Here, we introduce Memo-Patho, a deep learning framework for robust, alignment-free pathogenicity prediction of TMP variants. The core innovation is a within-protein, label-informed supervised contrastive pretraining strategy that learns sequence-encoded biophysical signatures distinguishing pathogenic and benign variants by directly comparing them within the same protein context. By fusing sequence-level representations from protein language models with local structural proxies derived from sequence, Memo-Patho achieves accurate predictions without multiple sequence alignments or experimental structures. Across diverse TMP benchmarks and under protein-level group splits, Memo-Patho consistently outperforms leading predictors, achieving up to 0.93 accuracy, and it transfers to an independent KCNQ1 ion-channel cohort without re-training. Its resource-efficient, alignment-free design enables routine large-scale screening when evolutionary or structural data are sparse. Conceptually, Memo-Patho addresses a key gap by directly learning discriminative, sequence-anchored signatures pertinent to TMP-specific constraints, offering a principled and generalizable foundation for research-use clinical variant triage and proteome-wide mutation-effect modeling.

This study explores the formation mechanisms of adolescent dependency on artificial intelligence and proposes a theoretical model of the "adaptive ecological trap" to explain its dynamic evolutionary process. Employing a grounded theory approach, in-depth interviews were conducted with 38 secondary school students who frequently used generative AI, and the data were analyzed through three-level coding. The results indicated that the formation of adolescent AI dependency is a systemic process that originates from gaps in real-world developmental needs and initiates compensatory use under the combined influence of technological allure and individual-environmental vulnerabilities. According to participants' accounts, this process may be perceived to erode key developmental capacities and potentially contribute to a self-reinforcing cycle. The model delineates a four-stage evolutionary pathway from adaptive use to increasingly impairing dependency: trap laying, triggering, tightening, and locking. This study emphasizes that AI dependency is not merely a behavioral addiction but an outcome of dysregulation within the individual-technology-environment ecosystem, characterized by dynamism, interactivity, and self-reinforcing properties. The findings call for collaborative interventions involving families, schools, technology platforms, and social policies. Furthermore, measures such as filling real-world developmental gaps, enhancing individual and environmental resilience, and disrupting negative feedback loops are crucial to assist adolescents in achieving healthy, autonomous development in the digital era.