Science Journals

Avoiding fertilization with genetically incompatible partners, whether too similar or too divergent, is a central challenge for sexually reproducing organisms. Selection can favor mechanisms acting before and after mating, with postmating processes potentially compensating for constraints on premating choice. In the postmating context, female reproductive fluid (FRF) can modulate sperm performance and bias fertilization outcomes, but its contribution to reproductive isolation remains unclear. We tested whether FRF mediates discrimination against heterospecific and related sperm in two naturally hybridizing sister species of swordtails, Xiphophorus birchmanni and X. malinche, that diverge in premating behavior towards heterospecifics. Effects of FRF differed sharply between species. In X. malinche, FRF enhanced the velocity of conspecific sperm relative to heterospecifics, consistent with postmating discrimination against hybridization. In contrast, FRF in X. birchmanni did not favor conspecific sperm. Evidence for inbreeding avoidance was weaker, and we found no indication of a trade-off between discrimination against genetically similar and dissimilar sperm. These results show that female reproductive fluid can serve as a rapidly evolving axis of reproductive isolation through postmating female choice.

Most species are geographically structured, leaving characteristic signatures in neutral regions of the genome. These signatures can be distorted when neutral regions are linked to deleterious mutations. In such regions, purifying selection can reduce genetic diversity through Background Selection (BGS) or, for recessive mutations, increase diversity through Associative Overdominance (AOD). While the effect of BGS and AOD are well characterized in panmictic populations, their effects remain largely unexplored in structured populations. Here, we investigated an Isolation with Migration model using forward simulations across a range of migration, selection, dominance, and recombination parameters. We first used a genotype-based approach to quantify the effects of deleterious mutations on standard summary statistics ({pi}, Dxy, FST, DAFi). We then showed that an Ancestral Recombination Graph-based approach, tracking tree sequences from a sample of one diploid per deme, recovers the same patterns while directly relating genetic variation to the underlying coalescent processes. When recombination is sufficiently low, we found a BGS-driven regime for weakly co-dominant mutations, characterized by lower diversity and increased genetic differentiation (FST). For recessive mutations, we first identified an AOD-driven regime, characterized by increased diversity and lower FST values followed by a transition to a subsequent BGS-driven regime. Genealogies were similarly impacted by deleterious mutations: BGS shrunk coalescent times and produces a shift towards lineage sorting topologies, while AOD stretched coalescent times and produces a shift toward incomplete lineage-sorting topologies. These patterns were weakened by gene flow, with FST and topologies remaining close to expected under neutrality, while diversity and coalescence times remained robust to demography. Our results provide clear evidence of BGS, AOD, and of their transition in a structured model with gene flow…

Susceptibility to viral infection varies widely but is not fully explained by genetics, immune status, or exposure level. We show that time of day strongly influences infection outcome, with up to 100-fold differences in enteric viral burden depending on infection timing. This temporal gating is abolished in mice lacking a functional circadian clock. We identify the antiviral transcription factor IRF1 as a direct target of the circadian transcription factor BMAL1, resulting in rhythmic expression of a basal antiviral gene program prior to infection. Loss of IRF1 eliminates this program and abrogates time-of-day dependent differences in viral replication. This circuit operates within intestinal myeloid cells, establishing a preexisting antiviral state. These findings indicate that the circadian clock programs host susceptibility in the intestine, before infection occurs.

Linkage disequilibrium (LD) makes causal GWAS variants indistinguishable from correlated neighbours; resolving them is the fine-mapping problem, and the challenge is species-specific: humans face dense ancestry-imbalanced LD, yeast and *Arabidopsis* exceptionally long LD, and crop germplasm sparse and fragmented annotations that defeat human-biobank curation pipelines. Bayesian fine-mappers integrate annotations as flat per-variant priors, discarding the relational structure linking variants to tissue-specific eQTLs, pathways and protein-protein interactions. Hierarchical belief propagation (HBP) on a variant-gene-pathway factor graph matches Bayesian baselines at 5-40x speed; an annotation-adaptive complement, graph-augmented fine-mapping (GAFM), wins 27-2 against SuSiE at weak signal and recovers *LDLR*, *APOE*, *LPL*, *GCKR* and *ANGPTL3* at single-variant resolution across four Pan-UK Biobank ancestries. On the 3,000 Rice Genomes grain weight + shape panel, mixture-prior posterior reweightings of GAFM/HBP and their ensemble (GAFM-MX, HBP-MX, ENS) reach 47.6% top-1-PIP exact-position recovery of 21 panel-matched stable QTNs - the highest of any method, exceeding SuSiE (28.6%) and SBayesRC (14.3%) - at 200-700x SuSiE's per-locus speed. Across 692 leads in four species, a non-uniform per-variant prior, not uniform high coverage, lets the graph break LD ties: adding a regulatory-element flag to an otherwise uniform human cache flips HBP narrower than GAFM from 0% to 88% on 321 Pan-UKB leads. These results recast multi-omics fine-mapping as a non-uniform-prior-curation problem rather than a uniform-coverage problem, and reframe post-GWAS analysis as message passing over biological structure rather than weighted regression on flattened annotations.

The thalamus is essential for learning, dynamically engaging with other subcortical and cerebral cortex regions throughout the learning process. Here, the thalamus serves as a critical connector hub and synchroniser within the thalamocortical system of the brain. However, whilst higher order thalamic nuclei are known to be particularly important for this process, the exact contributions of individual higher order and first order thalamic nuclei, alongside their individual involvement with cortical networks and subcortical regions, remains unexplored within the initial phase of learning. In light of this, we analysed fMRI data obtained within a paradigm which is designed to examine initial learning processes within feedback-driven stimulus-response learning, in order to explore thalamic contributions. We investigated dynamic learning-related functional connectivity alterations between various thalamic nuclei with other subcortical regions and cortical networks. Our results show that the initial phase of learning was associated with: (1) decreasing functional connectivity between thalamic nuclei and frontoparietal and cingulo-opercular networks, (2) increasing functional connectivity between thalamic nuclei with default mode and salience networks, (3) decreasing functional connectivity between thalamic nuclei and the putamen, and (4) decreasing functional connectivity amongst higher order thalamic nuclei. Furthermore (5) these dynamic alterations were associated primarily by mediodorsal thalamus. Altogether, these results indicate that higher order thalamic nuclei play a crucial role within initial learning and in the generation of novel goal-directed behaviour. This was demonstrated through enhanced functional connectivity with selected cortical networks which drive goal-directed behaviour, alongside decreased functional connectivity with striatal regions which drive motor selectivity.

The hippocampus is organized along dorsal-ventral and left-right axes, but whether and how these axes interact within defined neuronal populations across behavioral states remains unresolved. Here, we combined within-animal slice electrophysiology with dual-site fiber photometry to compare dorsal and ventral CA1 activity across contralateral hemispheric configurations in mice expressing CaMKII-jGCaMP8s and SynI-jRCaMP1b at distinct longitudinal sites. Ventral CA1 pyramidal neurons exhibited greater intrinsic excitability and stronger AMPAR-mediated synaptic responses than dorsal CA1 neurons. In vivo, CaMKII-defined pyramidal recordings during home cage rest revealed a left-biased event-rate asymmetry within dorsal but not ventral CA1, with no comparable asymmetry in pan-neuronal SynI recordings. Apparent dorsal-ventral differences in spontaneous event rate were therefore configuration-dependent and resolved into a hemispheric, cell-type-specific effect restricted to the CaMKII-defined population. Lead-lag analysis showed that dorsal-ventral temporal coordination was likewise reorganized across configurations and was restricted to pyramidal-cell-biased recordings. During open-field center entries, dorsal CA1 was preferentially recruited before entry across both configurations, whereas non-coordinated entries revealed a relative post-entry suppression of contralateral ventral CA1. Together, these findings suggest that dorsal-ventral CA1 organization cannot be inferred from hemisphere-pooled designs and identify a pyramidal-cell-specific left dorsal CA1 asymmetry as a structural feature that shapes both spontaneous activity and behaviorally driven recruitment along the longitudinal hippocampal axis.

Anxiety has been extensively studied in relation to memory, yet its dynamic association with spatial episodic memory in naturalistic clinical settings remains largely unexplored. We developed an anxiety-spatial-memory EMA protocol (asm-EMA) and deployed it in 30 epilepsy patients undergoing inpatient EEG monitoring, delivering combined momentary anxiety ratings and a validated spatial memory task pseudo-randomly every 90-150 minutes across multiple days. Subject-level asm-EMA means and session-to-session variability both correlated significantly with standard neuropsychological assessments, supporting the clinical validity of our design. Elevated within-person STAI-6 was selectively associated with faster retrieval responses, yet spatial memory accuracy was independent of all three anxiety measures, suggesting a shift in response strategy rather than memory impairment. Within-day anxiety showed short-term carryover between consecutive sessions, with little persistence beyond the next session. The asm-EMA protocol provides a feasible, autonomous framework for capturing moment-to-moment anxiety-memory dynamics in naturalistic settings.

Accurate and reproducible assessment of foliar disease severity is essential for evaluating the performance of heterogeneous plant communities and understanding host-pathogen interactions. However, traditional visual scoring methods remain subjective, with limited precision, and difficult to scale in large phenotyping experiments. Here, we present a semi-automated image analysis workflow designed to quantify multiple foliar disease symptoms simultaneously on wheat flag leaves sampled from varietal mixtures. The workflow combines three methodological components: (i) a standardized protocol for leaf sampling and imaging, (ii) supervised machine learning segmentation using Random Forest implemented in Ilastik to classify multiple symptoms (powdery mildew and yellow rust), and (iii) a graphical user interface facilitating pipeline deployment by non-specialist operators. To evaluate the influence of image representation on classification performance, four color spaces (RGB, HSV, HLS, LAB) were systematically compared. The approach was validated using images of durum wheat flag leaves collected from a field experiment assessing eight-way varietal mixtures under natural fungal pressure. Cross-validation against manually annotated images demonstrated high segmentation accuracy across all symptom. Comparison among color spaces revealed only minor differences in performance. Overall, this workflow offers a cost-effective, annotation-efficient and reproducible alternative to deep learning approaches, leveraging open-source and actively maintained tools while requiring limited training data and enabling objective, reproducible and scalable disease phenotyping.

RNA-seq experiments routinely identify thousands of differentially expressed genes, but translating these into biological insights and therapeutic hypotheses often requires integrating multiple tools. Existing web platforms such as iDEP, NetworkAnalyst, and GEPIA2 address individual steps, differential expression, network visualization, or TCGA queries, but lack a unified environment spanning raw data processing to clinical and pharmacological interpretation. TransXplorer (https://www.transxplorer.org) is a freely available web platform that addresses this limitation by integrating the complete RNA-seq analytical workflow. It supports processing from raw FASTQ files using HISAT2 or Salmon, as well as direct GEO dataset import with automated metadata handling. Differential expression analysis is implemented via DESeq2, edgeR, and limma-voom, followed by functional enrichment across more than 1,800 species using Bioconductor resources. Batch effects are automatically detected and corrected using a composite of PVCA, kBET, and Silhouette metrics without requiring predefined batch annotations. Downstream analyses include co-expression network construction (WGCNA), protein-protein interaction mapping (STRING), cell-type deconvolution, and transcription factor inference using integrated DoRothEA and TFLink resources. The platform further links gene signatures to drug candidates through DGIdb and OpenTargets and enables survival and tumour-normal comparisons across TCGA cohorts. Application to cardiac endothelial differentiation (GSE151427) and kidney renal papillary cell carcinoma (TCGA-KIRP) datasets demonstrates accurate batch correction, biologically consistent pathway enrichment, recovery of expected cell-type proportions, and identification of clinically relevant genes and drug candidates. TransXplorer is freely available without a login.

Chromosome-scale assemblies are increasingly available for non-model organisms, but functional annotation remains limited when deep evolutionary divergence erodes primary amino-acid sequence identity even though protein structural similarity can remain conserved. We present a hybrid annotation framework that decouples gene-model discovery from cross-species similarity assignment by combining Evo2-based ab initio prediction of exon-intron structures with ESM-2 protein-embedding-based structural similarity mapping. Applied to the sea lamprey, the framework derives high- or medium-confidence cross-species similarity assignments for 73,485 Evo2-derived translated protein models, including 35,395 high-confidence calls, and expands the deduplicated structural catalog to 31,286 loci, including 20,871 additions absent from the Ensembl baseline. A joint alignment-structure classification identifies 21,391 structurally supported catalog loci that a fixed human DIAMOND protein search does not confidently assign on its own, including 21,184 loci with no detectable human protein-sequence match and 207 loci with only low-confidence matches in the classical 20-30% amino-acid-identity twilight zone. These rescue-space totals describe catalog loci rather than validated one-to-one human-absent genes. In a single-cell RNA sequencing application, a stricter UTR-aware Ensembl+Evo2 reference improves gene recovery and expands the interpretable feature space of the lamprey immune compartment relative to the Ensembl baseline. This enables more resolved annotation of four transcriptionally defined immune cell states, including VLRA+-associated T-like and VLRB+-associated B-like programs together with oxidative iron-handling and iron-associated VLR-linked states. Together, these results show that structural protein signal often persists beyond the limits of pairwise sequence alignment and that an embedding-based annotation layer can extend that signal to improve downstream comparative and…

Terpenes constitute the largest and most structurally diverse class of plant secondary metabolites, with critical roles in plant-environment interactions and broad industrial applications. Although nuclear genome engineering of terpene pathways has been extensively explored, chloroplast genome engineering remains largely undeveloped, with all reported studies restricted to the model plant Nicotiana. Here we report successful chloroplast genome engineering for diterpene production in the crop plant potato (Solanum tuberosum). First, we identified the trnT/trnL plastomic locus as optimal for minimizing integration-associated growth penalties. Insertion of a bifunctional diterpene synthase gene into this plastomic site yielded transplastomic plants with successful diterpene production, but with reduced growth. The co-expression of a geranylgeranyl diphosphate synthase gene to enhance precursor supply restored normal growth while elevating diterpene accumulation. Transplastomic plants were otherwise agronomically comparable to wild-type. This work expands chloroplast engineering as a viable strategy for terpene pathway engineering in crop improvement and high-value terpene production.

Phyllanthus niruri (Phyllanthaceae) is a medicinally important herb known for producing phyllanthin, a bioactive dibenzylbutane lignan with reported hepatoprotective and antioxidant properties. However, the biosynthetic basis of phyllanthin production remains unresolved, largely due to the absence of a reference genome for the species. We report a chromosome-scale assembly of P. niruri generated by integrating PacBio HiFi long reads and Illumina short reads, followed by reference-guided scaffolding against Phyllanthus cochinchinensis. The assembly has an L50 of 7 and 97.6% BUSCO completeness. Annotation predicted 19,254 protein-coding genes, of which 91.1% were functionally annotated, with phenylpropanoid biosynthesis emerging as the most enriched specialized-metabolism pathway in the genome. Using pathway-guided genome mining, structural similarity analysis, and comparative metabolic reconstruction, we propose a putative biosynthetic pathway for phyllanthin originating from the phenylpropanoid-lignan branch through secoisolariciresinol-like intermediates followed by terminal O-methylation reactions. A total of 305 unique candidate genes associated with the proposed pathway were identified, including expanded families of dirigent proteins, peroxidases, secoisolariciresinol dehydrogenases, and O-methyltransferases. Comparative transcriptomic analyses across related Phyllanthus species further supported the proposed pathway through coordinated expression of lignan-associated genes and tissue-specific enrichment of O-methyltransferases. This work provides the first reference genome for P. niruri and a prioritized candidate gene set for functional characterization of phyllanthin biosynthesis.

Holcosus orcesi, the Orces Blue Whiptail, is a Critically Endangered lizard endemic to the upper Jubones River basin in southern Ecuador. Restricted to a narrow elevational range within semi-arid Andean shrublands, it represents one of the few montane members of a predominantly lowland lineage. Here we present the first high-quality reference genome for H. orcesi, generated using Oxford Nanopore Technologies long-read sequencing. The assembly spans 1.68 Gb across only 91 contigs, with an N50 of 76.2 Mb and a BUSCO completeness of 96.8%, making it among the most contiguous and complete squamate genomes to date. Structural annotation predicted 25,682 genes, of which 85% showed homology to known proteins and 45% were assigned Gene Ontology terms. Repetitive elements accounted for 46.3% of the genome, with LINEs representing the predominant class. This genome provides a foundational resource for future evolutionary, comparative and conservation-genomic research of H. orcesi and other mountain reptiles, enabling studies of population genomics, local adaptation, and genomic erosion in isolated populations. By expanding the genomic representation of tropical montane reptiles, this work helps address longstanding phylogenetic and geographic gaps in global biodiversity genomics and provides a foundation for evidence-based conservation of H. orcesi and related taxa.

Measles virus remains a significant global health threat, and despite the availability of an effective vaccine, measles cases continue to increase worldwide in recent years. Genomic surveillance has become an essential tool for monitoring virus circulation and investigating outbreaks. Here, we describe a wet laboratory method for whole genome sequencing of measles virus using a tiled amplicon approach and Illumina sequencing technology. A previously published Oxford Nanopore based tiled primer scheme was adapted to include both circulating measles genotypes and for use on the Illumina platform. Two Illumina library preparation kits, Illumina DNA Prep (IDP) and Nextera XT (XT), were evaluated for performance. The IDP kit demonstrated more complete genomes and consistent genome coverage compared with XT. Using quantified reference genomes, the limit of detection was determined to be 10,000 genome copies for genotype B3 and D8. Sequence accuracy was evaluated using previously characterized clinical samples and showed high concordance. This method provides a reliable and sensitive approach for measles virus whole-genome sequencing using Illumina platforms and is suitable for genomic surveillance applications.

Abstract As part of preparedness activities supporting pathogens classified under the UK High Consequence Infectious Diseases (HCID) framework, we previously evaluated both a whole-genome tiling amplicon sequencing scheme and a pan-viral hybridisation capture approach (TWIST-CVRP) for sequencing Andes virus (ANDV). In light of the recent outbreak, we make available viral sequencing datasets generated using a historical ANDV isolate (Chile, 1997). In addition, we provide an evaluation of tiling amplicon scheme performance and present recommended primer updates informed by in silico comparison with the recently released outbreak genome. These datasets are intended to support benchmarking, validation, and optimisation of bioinformatic pipelines across the community.

Biological sequences are known to be not random. Thus, the comparison of in silico restriction fragment distributions of random and biological sequences may be an indicator of this non-randomness. Our analyses show that for most of the tested combinations of restriction enzyme and genome sequence the fragments per Megabase of the biological sequence deviate at least more then 10% from the corresponding random sequence. This deviation goes into both directions, i.e. clearly increased values are as common as clearly decreased values. Although there is no species- or restriction-enzyme-specific effect, a clear impact of the GC content both of the restriction site and of the genome sequence can be seen. In contrast to the random sequences, the genome sequences show distinct peaks in their fragment length distributions, hinting to repetitive elements such as transposons.

As one of the earliest-diverging multicellular eukaryotic lineages, the bladed Bangiales (Rhodophyta) possess a deep evolutionary history with a central role in the multi-billion-dollar global seaweed aquaculture industry. Although North Atlantic representatives are emerging candidates for regional mariculture, the scarcity of high-quality genomic resources for these taxa hinders both fundamental research and commercial optimization. To address this, we present the first chromosome-level genome assemblies for two native European species: Porphyra dioica (150.44 Mbp) and Porphyra linearis (95.22 Mbp). By integrating Oxford Nanopore Technologies (ONT) long-read sequencing with Hi-C proximity ligation, we generated highly contiguous nuclear genomes resolved into five chromosomes. Structural gene models were predicted through the BRAKER3 pipeline, identifying 12,548 and 10,382 protein-coding genes for P. dioica and P. linearis, respectively. Subsequent homology-based functional annotation characterized 57.4% and 59.8% of these predicted proteins. Supplemented by circularized organellar genomes, these reference genomes provide a critical framework for future research, enabling comparative studies of Atlantic-Pacific divergence and facilitating the development of selective breeding programs for sustainable European aquaculture.

LINE-1 retrotransposons are the only autonomous mobile elements still active in human genomes and remain a potent source of mutation, genome remodeling, and disease risk. However, young, full-length, potentially active copies (the elements most likely to shape present-day genomes) have been largely inaccessible to population-scale analysis because they are long, repetitive, and poorly resolved by short-read sequencing. Here, we use 47 phased long-read assemblies from the Human Pangenome Reference Consortium, representing 94 haplotypes, to build an allele-resolved view of recent human LINE-1 evolution. We identify 13,617 LINE-1 alleles with intact ORF1 and ORF2 across 683 unique insertion sites, revealing that every genome carries a distinct repertoire of potentially active source elements. These intact LINE-1 profiles recapitulate broad human population structure while exposing a large, rare, and population-enriched reservoir of mobile-element diversity missed by single-reference approaches. We also resolve a structurally variable chromosome 11 LINE-1 array, demonstrating that local duplication and rearrangement can amplify LINE-1 sequence independently of canonical retrotransposition. By comparing full-length LINE-1 sequences, we define activity signatures that separate ancient remnants from recently expanding lineages and uncover young LINE-1 groups whose activity is not fully explained by canonical subfamily labels. Sequence-network analyses further reveal a dynamic history of lineage turnover, in which successful source elements rise, seed new insertions, and are replaced by descendants marked by specific nucleotide changes. Together, these data transform human LINE-1s from a repetitive background into a resolved evolutionary system, linking insertion polymorphism, coding potential, population history, and recent retrotransposon adaptation. Our findings establish the human pangenome as a framework for discovering active source elements and for testing how mob…

Gross chromosomal rearrangements are a hallmark of many diseases and cancers. The study of their biogenesis and the mechanisms underlying their formation is greatly facilitated by the availability of genetic reporter assays in model organisms. We present here a novel GCR assay developed in fission yeast, a highly relevant model for understanding genome instability related to human biology. The reporter employs canavanine counter-selection to detect GCRs within a chromosomal context. Using this assay, we identified natural hotspots for GCRs, including inverted long terminal repeats (IR-LTRs). Structural analysis of GCR events showed that IR-LTR-induced GCRs mainly result in either terminal deletions with adjacent inverted duplications or repair via long-range break-induced replication (BIR). Deleting IR-LTRs reduces the GCR rate and reveals another hotspot driven by BIR between homeologous aldo/keto reductase genes on opposite arms of chromosome I. This is the first evidence that BIR can occur in S. pombe on long tracks reaching up to 600 kb. Besides highlighting genome rearrangement hotspots, the assay also identifies regulators of genome instability in fission yeast. Loss of Nup132, a component of the nuclear pore complex, increases IR-LTRs-induced GCRs, while the budding yeast homolog Nup133 has no effect on the stability of a structurally similar IR. In contrast, disrupting djc9, which encodes a conserved histone H3-H4 binding protein, decreases GCR rates. Overall, this sensitive GCR assay enables the identification of factors that control spontaneous and fragile motif-induced chromosomal instability, including those conserved in humans but lost through evolution in other organisms.

Maximum utilization of existing genetic variability in a breeding program depends on the efficient classification of the inbred lines into heterotic groups, particularly under stress conditions. This study applied practical breeding approaches to determine the mode of genetic inheritance for Striga resistance and proposes a weighted heterotic grouping method based on the general combining ability of multiple traits (WHGCAMT) and compares its effectiveness with other existing methods in classifying the inbred lines into heterotic groups in Striga-infested and optimum environments. Using Diallel design IV, 300 crosses were generated from 21 inbred lines and 4 standard testers. The crosses, along with six checks, were evaluated in an 18 x 17 alpha lattice design with two replications at two locations, in both artificial Striga-infested and Striga-free environments. The inbred lines were genotyped using DArTtag SNP markers. Phenotypic and genotypic data were analyzed using R. Analysis of variance revealed significant mean squares for hybrid, general combining ability (GCA), specific combining ability (SCA) and their interactions with environment. Significant positive and negative GCA and SCA effects were detected for grain yield and other measured traits. However, a larger proportion of additive gene action than non-additive gene action was observed for grain yield and most measured traits. The analysis of molecular variance also showed substantial genetic differences within and between clusters. Except for HSCA, the mean grain yield between the inter-group and intra-group hybrids was significant for each method. Pairwise comparison of the inter- and intra-group hybrids of all the methods showed significant differences between the WHGCAMT and all other methods in most cases. WHGCAMT consistently produced higher-yielding inter-group hybrids and lower-yielding intra-group hybrids, achieving breeding efficiency improvements of 55.8%, 4.3%, 15.7%, and 11.4% over the HSCA…

LD Score Regression (LDSC) is a prominent method, which estimates whole-genome SNP heritability from summary statistics via the slope of a linear regression of GWAS test statistics corresponding to a trait of interest against LD scores. It was claimed by the LDSC authors that the free intercept in the regression accounts for confounding bias such as population stratification. In this study, we argue that the intercept in LDSC must be fixed to 1 for accurate SNP heritability estimation. We show both theoretically and with simulations that the estimated intercept does not accurately capture population stratification effects, and that it adversely affects the accuracy of the heritability estimate introducing bias and increasing variance. Fixing the intercept to 1 eliminates bias and reduces variance when no population stratification is present. On the other hand, under population stratification, LDSC is biased with both the free and the fixed intercept. Additionally, we show that estimated standard errors in LDSC are underestimated, potentially leading to false-positives in downstream GWAS analyses.

Gene duplication is a major driver of evolution, yet how it generates fundamentally new molecular functions remains poorly understood. Here, we show how such novelty arose in KLMT-1, a selfish toxin that causes genetic incompatibilities in Caenorhabditis tropicalis. KLMT-1 evolved via duplication of an essential tRNA synthetase but, strikingly, lost its ancestral role in tRNA biology and translation. Instead, KLMT-1 localizes to centrosomes, where it targets Aurora kinase A (AIR-1). This innovation is mediated by a three-amino acid insertion that extends a beta-hairpin loop, enabling electrostatic interaction with a regulatory interface on the kinase. Our results demonstrate how changes in selective pressure, combined with minimal modifications in neutrally evolving regions, allow duplicated proteins to access new functional space and evolve entirely new molecular activities.

In modular organisms, where growth and fragmentation blur the boundaries between individuals, the interplay between asexual and sexual reproduction creates complex fitness trade - offs. Life - history theory predicts that resources allocated to one fitness component necessarily reduce investment in others, yet detecting these trade - offs in wild populations of clonal organisms remains challenging. Phenotypic plasticity can enhance survival, yet its influence on reproductive capacity and life - history trade - offs remains poorly understood. Using a fully crossed reciprocal transplant design, we tracked 263 colonies of the branching coral Acropora cervicornis across nine reef sites over 42 months, investigating relationships between fragmentation, morphological plasticity, and the capacity for sexual reproduction. Breakage patterns reflected both environmental and genetic factors. Primary branch breaks created a "double negative" effect - simultaneously more than doubling mortality risk and delaying attainment of a validated reproductive size class by ~40%. Conversely, higher morphological plasticity in surface area - to - volume ratio accelerated sexual maturation up to 6 - fold, counteracting the negative effects of fragmentation. In parallel, a simple demographic model parameterized with published fecundity data estimated that primary breakage reduces expected cumulative reproductive output by ~58%, a result robust across a wide range of parameter assumptions. These results demonstrate a fundamental reproductive trade-off in which asexual reproduction through fragmentation undermines sexual reproductive potential by reducing colony size. Moreover, our findings reveal that fragmentation susceptibility is broadly heritable and subject to selection, and identify a compensatory mechanism through which plasticity enhances fitness beyond immediate survival.

Ecological specialization emerges when adaptation to a focal context increases fitness in that context relative to others. Experimental evolution has been widely used to study microbial specialization in abiotic environments but not predatory specialization. Here we demonstrate evolutionary specialization by a bacterial predator and characterize associated diversification of its predation profile. Populations of the bacterium Myxococcus xanthus evolving on single, non-evolving prey species diverged in predatory performance, showing increased performance on their home prey relative to their common ancestor, and relative to foreign prey not encountered during adaptation. Adaptation to the single-prey environments resulted in striking radiation of performance profiles across a diverse panel of foreign prey that was shaped interactively by selection, chance, and indirect effects, with home-prey identity modulating the degree of stochastic indirect diversification. Despite a great diversity of indirect evolutionary effects, correlated evolution was net-positive, yielding positive predatory specialization as the general outcome. Genomic evolution mirrored phenotypic evolution in that degrees of genomic parallelism differed as a function of home-prey identity. These findings show that adaptation to even simple biotic conditions can generate great ecological and behavioral diversity, linking direct selection, deterministic indirect effects of adaptation, stochasticity, and the origins of predator specialization and diversification.

Allostery enables proteins to couple environmental signals to functional outputs, yet how allosteric mechanisms diversify during evolution remains poorly understood. Here, we address this question in the ubiquitous and functionally diverse arsenic repressor (ArsR) superfamily by integrating information-theoretic bioinformatics, structural characterization of DNA recognition and NMR measurements of fast internal dynamics. We identify conserved residues that define the structural scaffold of ArsR proteins and subfamily-specific positions that encode inducer and DNA specificity. In the persulfide sensor SqrR, the crystal structure of the DNA-bound complex reveals how operator specificity is encoded by a limited set of residues, consistent with sequence-derived predictions functionally validated by in vitro transcription assays across divergent ArsR regulators. We further show that allosteric inhibition of DNA binding in SqrR occurs without large-scale conformational rearrangements and is instead associated with changes in internal dynamics, as previously observed for the zinc sensor CzrA. Together, these results support a model in which conformational entropy preserves allosteric connectivity while relaxing sequence constraints, thereby enabling functional diversification within a protein superfamily.