Self-assembly of the domestic cat hepadnavirus core antigen (DCHcAg) into virus-like particles (VLPs).

Extracellular vesicles (EVs) are involved in diverse functions in nature, from biogeochemical cycles to pathogenesis. Here, we investigate whether taxonomy represents a boundary that constrains the uptake of vesicles between different marine species. We focus on Vibrio spp. since they are ubiquitous and play different roles, including in diseases affecting humans and marine organisms, such as corals. Spectral flow cytometry data showed intra- and inter-species uptake of EVs from the pathogenic Vibrio kanaloae and Vibrio coralliilyticus by different Vibrio strains and species, including other marine and non-marine Gram-negative and Gram-positive recipient bacteria. EV fusion efficiencies with recipient cells ranged from 53% to 99%, although these values may vary depending on the nutrient status of the cells. Data suggest that EV fusion between cells from different taxa can occur regardless the phylogenetic distance between the EV donor and recipient cell and the quantity of EVs added. Data revealed that nutrient conditions did not play a significant role in the number of EVs released by the analyzed Vibrio spp., although differences were observed in the amount of DNA exported in EVs (p-value = 0.02), with more bulk vesicular DNA cargo in diluted nutrient conditions. Nearly the entire genome of the targeted Vibrio spp. was detected in the EV fraction. Our data suggest that taxonomic distance between the EV donor and recipient cell is not a major boundary.

The guts of many animals are colonized by host-specific microbes, yet the extent to which host filtering (host-derived constraints) shapes microbial colonization and host specificity remains poorly understood. Here, we used gnotobiotic honey bees (Apis mellifera) as a model system to systematically assess the colonization potential of a phylogenetically and ecologically diverse panel of 56 bacterial strains, spanning native symbionts, opportunistic bee-associated taxa, gut microbes from other bee species, and non-bee environmental isolates. Bacterial load and colonization frequency were quantified by strain-specific qPCR seven days post-inoculation, in monocolonization and in the presence of a synthetic community composed of native honeybee core bacteria. Bacterial load was highest for native strains and declined with increasing phylogenetic distance from native symbionts. Co-colonization with the synthetic community reduced bacteria load across all groups, but native strains were least affected. Across strains, completeness of KEGG metabolic pathways correlated with bacterial load in some ecological groups; however, metabolic capacity alone did not fully explain colonization patterns, either in monocolonization or under competitive conditions. A key finding was that in vitro sensitivity to antimicrobial peptides (AMPs; apidaecin, abaecin, defensins, and hymenoptaecin) varied widely among strains and was highest in closely related bee-associated bacteria. Even highly successful colonizers such as Gilliamella and Snodgrassella were AMP-sensitive. AMP sensitivity showed a negative correlation with bacterial load, but not with the frequency of host colonization. These findings suggest that AMPs modulate symbiont abundance rather than acting as strict barriers to colonization. Overall, our results reveal that host filtering in the bee gut is multifaceted, integrating immune-mediated barriers, microbial traits, and competitive interactions.

Darwin's Naturalization Conundrum holds that both functional similarity and distinctiveness can facilitate biological invasions: invaders similar to natives may succeed through preadaptation to local abiotic conditions, whereas functionally distinct invaders may succeed by reducing competition. Yet the contexts in which either mechanism dominates are unclear. Prior research has primarily attributed variability in native-invasive differentiation to shifts in the balance between biotic and climatic barriers to invasion from local to regional scales. However, similarity and distinctiveness are frequent at both local and regional levels, indicating key drivers of native-invasive differentiation remain overlooked. Crucially, theory and evidence show that as climatic stress increases, competition weakens. This implies that harsh climates should favor invaders functionally similar to natives, whereas mesic climates should favor distinctiveness. However, this hypothesis has not been tested across climate regimes and functional traits. We addressed this gap by combining models of species distributions and flowering phenology for 2,810 species across the United States, estimating phylogenetic distance and phenological differentiation between natives and invasives relative to differentiation among co-occurring natives. In warm, humid regions, invasives flowered earlier, less synchronously, and were more distantly related to natives. In cold or dry regions, they flowered at similar or later times, more synchronously, and were more closely related than natives themselves. Across all climates, invasives consistently exhibited longer flowering durations, with little evidence of greater phenological plasticity. These findings reveal that Darwin's Conundrum reflects a predictable continuum shaped by environmental context, highlighting climate as a key axis of invasion success.

Animals maintain close associations with diverse microbiota that inhabit their digestive tracts, and these associations can profoundly affect host physiology and fitness. Gut microbiome composition is shaped by both host traits and environmental factors, yet the relative importance of these forces remains unclear in many taxa, including squamate reptiles (lizards and snakes). To address this gap, we analysed the gut microbiomes of seven species of Anolis lizards in the lowland tropical rainforest of central Panama. We sought to determine how environmental and host species characteristics shaped gut microbiome composition. Specifically, we examined (1) interspecific variation in the anole gut microbiome, (2) the relative roles of environment and host species in shaping gut microbiomes across two study locations, and (3) patterns of phylosymbiosis. We found that host-related factors (species identity, body size, and phylogenetic distance) were significant predictors of the composition of Anolis gut microbiomes. However, environmental factors, including locality and year of sampling (associated with temperature, humidity, and precipitation), also exerted significant effects. We detected evidence of phylosymbiosis, but this pattern was moderate, possibly due to the strong effect of environmental variation. Our work contributes to the growing body of literature on lizard gut microbiomes by using comparative observations across habitats and species to identify the factors that shape these communities in the wild.

Multiscale evolution of the 3D genome.

Correction: Niche partitioning and phylogenetic distance of rotifer species revealed by the four-year temporal dynamics in a small mountain lake

Anaerobic digestion (AD) of food waste (FW) is a key wate-to-energy strategy, yet daily biogas yield is often challenging to sustain, partly due to a limited understanding of the internal methanogens and their functional divergence. Here, we investigated seven full-scale mesophilic FW-AD systems distributed across China along a broad latitudinal gradient (>2,800 km), linking methane production variations (0.38-2.11 m3/m3•d-1) with the phylogenetic distributions of methanogens and their methanogenic genes. We found that hydrogenotrophic and aceticlastic pathways were ubiquitous, whereas methylotrophic methanogenesis showed regional enrichment in warmer regions, reflecting persistent influences of climate-associated upstream conditions on downstream methanogenic communities. Gene-level phylogeny of methanogenesis-related alleles, rather than species-level phylogeny, closely tracked biogas yield variation (Mantel's P < 0.05) and showed consistently stronger associations than gene-level compositions (mean standardized total effect: 0.491 vs. 0.298, P < 0.01). Higher methane yields (1.61 vs. 0.61 m3/m3•d-1 in high- vs. low-performing systems, P < 0.01) were significantly associated with reduced Faith's phylogenetic diversity (1.82 vs. 2.30, P < 0.01) and tighter clustering (mean pairwise phylogenetic distance, MPD: 0.25 vs. 0.30, P < 0.01) of methanogenic gene variants, suggesting that phylogenetic coherence may reflect ecological filtering favoring efficient methanogenesis, albeit at the expense of functional redundancy. These findings highlight gene-level trait phylogeny as a potential proxy for functional robustness, offering a framework for ecological design of AD microbiomes.

ABSTRACTUnderstanding the intricate dynamics of biodiversity within and across riverine ecosystems, influenced by geological history and environmental factors, is crucial for effective conservation and management strategies. Italy, particularly the Ligurian region, harbors diverse freshwater fish communities and populations shaped by unique geological and hydrological conditions. Here, we investigated the suitability of environmental DNA (eDNA) metabarcoding to identify inter‐ and intraspecific diversity patterns of riverine fish populations in drainage basins on both sides of the main drainage divide (MDD) between the Adriatic and Ligurian river basins in Northern Italy. We collected 96 aquatic eDNA samples across 48 riverine sites, amplified them using a cytochrome b primer pair, and denoised the sequences to retrieve amplicon sequence variants (ASVs). We calculated communities' phylogenetic distance with betaMPD based on genetic distances derived from the ASVs, combined them with conductance‐based landscape metrics, and applied generalized dissimilarity models to assess spatial genetic structure. Our results reveal genetic differentiation among populations of several fish species, with some displaying clustering patterns across the main drainage divide and isolation by distance patterns. Overall, taxon richness was higher on the Adriatic side (12.82 ± 3.57; 26 unique taxa) than on the Ligurian side of the MDD (8.35 ± 3.66 SD, 25 unique taxa), but the other way around for ASV richness across all species (Ligurian side: 51.94 ± 25.79, 308 unique ASV, Adriatic side: 68.00 ± 32.97, 274 unique ASV). Our findings highlight the effectiveness of eDNA metabarcoding in uncovering various facets of diversity, shedding light on hidden genetic diversity among ASVs, and revealing significant spatial genetic structuring in freshwater fish populations across multiple species.

While non-coding G-quadruplexes (G4s) act as conserved regulatory elements when located in gene promoters and splice sites, the evolutionary conservation of G4s in protein-coding regions remains under-explored. To address the evolutionary dynamics acting on coding G4s, we mapped and characterized potential G4-forming sequences (PGQS) across the orthologous coding DNA sequences (CDS) of twenty-two primates. We found that the PGQS count correlated with the number of available orthologs and, to a lesser extent, with phylogenetic distance from humans. PGQS motifs showed high co-localization, especially among closely related species. Thermodynamic stability, inferred from the minimum folding free energy, emerged as an important factor associated with evolutionary patterns: low stability PGQSs (minimum folding free energy ≥ –10 kcal/mol) were more conserved, whereas high stability PGQSs (minimum folding free energy ≤ –30 kcal/mol) showed reduced conservation, although both categories remained more conserved than the CDS baseline. Consistently, indel scores were negatively correlated with the minimum folding free energy of PGQSs, suggesting an association between stable motifs and insertion or deletion events. In line with this, G-rich tandem repeats exhibited elevated indel mutagenesis, consistent with their propensity to fold into highly stable G4s. Altogether, these findings reveal that PGQSs simultaneously act as conserved elements and sources of structural instability, reflecting antagonistic selective pressures that preserve sequence function while generating instability through structure formation.

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

The Mycobacterium tuberculosis complex (MTBC) includes ten human-adapted lineages with varying geography and pathogenicity. Lineage 1 (L1) shows low virulence while Lineage 2 (L2) is hyper-virulent, more transmissible, and associated with drug-resistance. We performed comparative analyses integrating whole-genome sequencing with transcriptomic and proteomic profiling of L1 and L2 clinical strains under two in vitro growth conditions. Transcript-protein correlations varied by strain and gene category, suggesting lineage-specific post-translational regulation. Expression differences scaled with phylogenetic distance, one in three SNPs affected gene expression. A new transcriptional regulatory model identified master transcription factors, linked to the sigma factor network, whose targets were differentially expressed between L1 and L2. For instance, DosR regulon proteins had higher basal levels and exhibited a stronger nitric oxide response in L2. Time-course experiments involving LysG (Rv1985c) induction and wild-type H37Rv under hypoxia and subsequent reaeration confirmed that LysG contributes to reduced metabolic activity, thereby promoting increased tolerance to the novel tuberculosis drug bedaquiline in L2 strains relative to L1. Overall, our findings show how limited genetic variation in the MTBC can yield major phenotypic differences through differential regulation of key transcriptional networks.

Comparison of genetic structure of multiple Betula species across China reveals ploidy variation, latitudinal diversity patterns and interspecific hybridization and admixture.

On evolutionary timescales, brain circuits adapt to support survival in each species' ecological niche. While some anatomical aspects of neural circuitry are conserved across species with distant evolutionary origins, each species also exhibits specific circuit adaptations that enable its behavioral repertoire. It remains unclear whether homologous brain regions leverage analogous neural computations as different species perform common behaviors such as reaching and manipulating objects. Here, we directly assessed conservation of neural computations using intracortical recordings from mouse, monkey, and human motor cortex-a homologous region across many mammals-during motor behaviors crucial for survival. We hypothesized that, despite their phylogenetic distance, rodents and primates produce movements through conserved neural computations implemented by motor cortical population dynamics. Remarkably, we found that movement-related neural dynamics were highly conserved across species, while variations in behavioral output were uniquely captured in neural trajectory geometries. Strikingly, neural dynamics during movement across species were more conserved than those across brain regions in the same human and between motor preparation and execution in the same monkeys. Lastly, through manipulation of neural network models trained to perform reaching movements, we reinforce that conservation of neural dynamics across species likely stems from shared circuit constraints. We thus assert that evolution maintains neural computations across phylogeny even as behavioral repertoires expand.

Foliar fungal communities are essential components of the plant microbiome, playing a vital role in maintaining plant health and influencing ecosystem dynamics. Despite increasing interest in plant–microbe associations, the drivers shaping foliar fungal community composition remain poorly understood, including the roles of host phylogeny, functional traits, and belowground mycorrhizal symbiosis. We used the MycoPhylo experimental field, in which plant species are planted in a replicated, phylogenetically diverse design, to investigate the influence of host plant identity, mycorrhizal type, and leaf functional traits on foliar fungal assemblages. We examined foliar fungal communities across 158 plots representing 110 distinct plant species using a metabarcoding approach. The resulting operational taxonomic units (OTUs) were dominated by Dothideomycetes (44.5%), Tremellomycetes (12.7%), and Taphrinomycetes (9.0%). Functional guild analysis revealed that plant pathogens and saprotrophs were the most abundant ecological groups. Foliar fungal alpha diversity and community composition were significantly influenced by plant growth form and mycorrhizal association. Although plant deciduousness did not affect fungal richness, it significantly affected fungal community composition. The measured leaf traits (hairiness and thickness) showed the least influence on fungal richness. Mantel tests revealed weak, guild-dependent relationships between host phylogenetic distance and foliar fungal community dissimilarity. Moreover, plant phylogenetic eigenvectors accounted for up to 25.8% of the variation in fungal richness. These findings indicate that host phylogeny and plant traits contribute to—but do not solely determine—the structure of foliar fungal assemblages under field conditions.

The parentage of the widespread allopolyploid Drosera anglica, a member of the carnivorous sundew genus, remains uncertain despite over 100 years of morphological, cytological, and, more recently, molecular study. Using transcriptomic and genomic data from 12 species of Drosera sect. Drosera, including four D. anglica populations and a population sometimes identified as disjunct D. intermedia, we assembled genes in HybPiper and phased sequences in HybPhaser. We estimated species relationships with phylogenetic and pairwise genetic distance methods and ploidy with heterozygosity and flow cytometry measurements. Additionally, we expanded represented taxa by analyzing new and previously published rbcL sequences. Sequences from phased subgenomes of D. anglica were highly similar to D. rotundifolia (99.60-99.80%) and D. linearis (99.79-99.95%) and showed no evidence of multiple origins despite sampling across North America, Europe, and Hawaii. Additionally, the disjunct D. intermedia from Idaho had been misidentified and is D. anglica. Sequences from the nuclear ribosomal region and rbcL of D. anglica were nearly identical to D. linearis despite their chromosomes mispairing during meiosis and counter to interpretations of limited Sanger sequencing. Drosera anglica is intermediate between its parental lineages in leaf shape and microhabitat; however, across D. sect. Drosera, neither leaf shape nor biogeographic distribution was a reliable indicator of phylogenetic relationships. Drosera anglica arose from allopolyploidy between the D. linearis lineage, representing the plastid and dominant ribosomal donor, and the D. rotundifolia lineage. Our study demonstrates the importance of taxon sampling and careful examination of complex phylogenomic data and presents an exemplar of analyzing allopolyploid relationships.

Biological invasions severely threaten ecosystems and their underlying drivers remain a subject of ongoing inquiry in ecology. Four mutually exclusive invasion hypotheses, biotic acceptance and resistance hypotheses and Darwin's preadaptation and naturalization hypotheses, have long drawn extensive attention. Furthermore, human activities and environmental factors are also widely recognized as key drivers of biological invasions. While integrative analyses of the aforementioned biotic and abiotic factors influencing biological invasions have been conducted previously, systematic global-scale analyses for freshwater fishes remain limited, constraining our understanding of large-scale invasion patterns in this taxon. Here, we leveraged a comprehensive database with taxonomic, functional, and phylogenetic data for 5245 freshwater fish species across 1411 global river basins to explore ecological correlates of non-native fish establishment. Specifically, we used taxonomic, functional, and phylogenetic facets of biodiversity to comprehensively quantify native communities (testing biotic acceptance and resistance hypotheses) and relatedness between native and non-native communities (testing Darwin's preadaptation and naturalization hypotheses). We further extracted environmental and anthropogenic variables across global rivers to assess external predictors of non-native fish establishment. Our results primarily supported Darwin's naturalization hypothesis: at the global level, native fish community invasibility peaked when non-native species exhibited great functional or phylogenetic distance from native communities, suggesting distantly related non-natives likely had unique traits or strategies to exploit vacant niches. Meanwhile, climatic factors also emerged as key drivers of global fish invasion patterns. At the biogeographic realm level, the determinants of fish invasions varied among the six realms, highlighting the complexity and regional specificity of biological invasions. However, our findings were based on correlational patterns of established non-native species at the basin scale and thus cannot establish definitive causal relationships between the identified drivers and establishment success. Future experimental manipulations at finer spatial and temporal scales are therefore required to validate the correlations observed in this study.

Variation in the material properties of bone has been linked to functional activity in mammals, including primates. This coheres to the paradigm that skeletal morphology, in general, provides insight into species-specific physical activity patterns. The role of phylogenetic history in conditioning bone material properties, however, is largely unexplored, despite consensus that patterns of morphological variation should be sensitive to degrees of relatedness among sampled taxa. We collected microindentation hardness data (a measure of bone material stiffness) from the mandibles of five sympatric primate species from Taï Forest, Côte d'Ivoire to test the hypothesis that degree of relatedness, rather than species differences in diet and feeding behavior, is more strongly associated with bone material variation. This hypothesis is tested using a generalized linear mixed model with Bayesian inference. Phylogenetic distance has a significant association with bone stiffness, with colobines exhibiting more compliant bone than cercopithecines. The alternative hypothesis, that differences in dietary mechanical demands are reflected in bone stiffness variation, is not supported. While these findings suggest a role for phylogeny in constraining skeletal adaptation, a functional explanation is not necessarily precluded. Ingestive behavioral differences between subfamily members may provide a biomechanical framework for explaining what is, at present, a nebulous invocation of phylogenetic "baggage."

The elasmobranch dermal microbiome may be important for buffering effects of environmental stress on host health and population viability via functional metabolic interactions. Dermal microbiomes among elasmobranch orders co-vary with host phylogeny (phylosymbiosis), indicating functional co-evolution with their hosts at deep phylogenetic splits. However, the extent of phylosymbiosis and potential for functional co-evolution within particularly species-rich elasmobranch families remains unknown. Here, we re-analyse Illumina amplicon sequence data from the 16S rRNA gene from eight Carcharhinid shark species (plus one Ginglymostomatid outgroup) across six independent studies and explicitly examine the extent of phylosymbiosis in dermal microbiomes within this family. We found extensive divergence in operational taxonomic unit (OTU) abundance and functional metabolic capacity between studies, driven by disparity in OTU sharing and probably reflecting geographical and seasonal factors. Total microbiome structure was incongruent with shark phylogeny, providing no evidence for phylosymbiosis when considering all species and OTUs. However, using bootstrapping and subsampling methods, we identified several subsets of OTUs where Bray–Curtis dissimilarity supported perfect topological congruence with shark phylogeny or strong associations with phylogenetic distances, but not both. Partial Mantel tests identified ten candidate OTUs that supported a moderately strong signal of phylosymbiosis across all shark species and included the immunostimulant skin symbiont genera Lactiplantibacillus and Alcaligenes. Overall, this provides provisional evidence for phylosymbiosis in a minority fraction of the elasmobranch dermal microbiome within the Carcharhinidae family and will necessitate coordinated large-scale studies to establish the generality of these findings.