The complex historical phenomenon known as Greek colonization refers to the strategic establishment of new settlements (colonies) from the 8th to the early fourth century BCE. Unlike earlier migrations, this process was planned and driven by the need to expand trade, access resources, and develop economic as well as political networks. Corinth, a prominent commercial center in southern Greece, constitutes a prominent example for initiating colonization. By founding colonies, Corinth established a safe and continuous route for moving goods along the coasts of western mainland Greece and the Adriatic. Amvrakia was one of Corinth's principal colonies along this route in northwestern Greece. Founded in the seventh century BCE, Amvrakia was characterized by a strong dependence on its metropolis (Corinth). Here, we aim to investigate the genetic relationships between the Corinthian metropolis and the Amvrakia colony, the contribution of the local population to the founding genetic pool, as well as the demography of Amvrakia in subsequent periods. During its foundation in the Archaic period, Amvrakia appears to have been shaped by genetic influences from a single source. This source migrated from the Corinth territory, represented by the Archaic Tenea population and is supported via an Identity By Descent (IBD) analysis. A direct ancestry from Late Bronze Age (LBA) Greece, including a local LBA population represented by the Ammotopos site located in close proximity to Amvrakia, was not inferred despite conducting a plethora of independent population genomics analyses. During the subsequent Classical and Hellenistic periods, the population of Amvrakia appears to have only slightly differentiated and evidence of genetic continuity over time is observed. The migration of Corinthians to Amvrakia was the major contributor to the initial genetic pool of the colony, indicating that the Corinthian colonization included both genetic and cultural transmission between the metropolis and its colony.

Plasmids play a pivotal role in the emergence of multidrug-resistant and pathogenic bacteria, posing significant clinical challenges. However, the rapidly growing number of unannotated plasmids necessitates comprehensive characterization of their diverse properties. Here, we present PlasRAG, a tool that integrates multi-faceted property characterization of query plasmids and plasmid DNA retrieval based on textual queries. PlasRAG employs a bidirectional multi-modal information retrieval model that aligns DNA sequences with textual data, effectively overcoming the limitations of traditional approaches. Rigorous experiments demonstrate that PlasRAG delivers robust performance and enhanced analytical capabilities, underscoring the effectiveness of its architectural design.

Genetic variants that are associated with phenotypic variability, or variance quantitative trait loci (vQTLs), have been detected for multiple human traits. Gene-environment interactions can lead to differential phenotypic variability across genotype groups, therefore, genetic variants that interact with environmental exposures can manifest as vQTLs. Although changes in DNA methylation variability have been observed in several diseases, vQTLs for methylation levels (vmeQTL) have not yet been explored in depth. We optimize the value of monozygotic twin studies to identify and replicate vmeQTLs for blood DNA methylation variance at 358 CpGs in 988 adult monozygotic twin pairs from two European twin registries. Over a third of vmeQTLs capture identical vmeQTL-environmental factor interactions in both datasets, and the majority of interactions are observed with blood cell counts. Correspondingly, over 60% of CpGs affected by genotype-monocyte and genotype-T cell interactions replicate as CpGs affected by genetic effects in the relevant cell type in an independent dataset. Most vmeQTLs also replicate in 1,348 UK non-twin adults and show longitudinal stability in a sample subset. Integrating gene expression and phenotype association results identifies multiple vmeQTLs that capture GxE effects relevant to human health. Examples include vmeQTLs interacting with blood cell type to influence DNA methylation in FAM65A, NAPRT, and CSGALNACT1 underlying immune disease susceptibility and progression. Our findings identify novel genetic effects on human DNA methylation variability within a unique monozygotic twin study design. The results show the potential of vmeQTLs to identify gene-environment interactions and provide novel insights into complex traits.

The role of transposable elements (TEs) in host adaptation has gained interest in recent years. Individuals of the same species undergo independent TE insertions, providing genetic variability within populations, upon which natural selection can act to foster adaptation to environmental conditions. As de novo assembled genomes are becoming increasingly affordable, helping to overcome the bias introduced by relying on a single reference genome, there is a growing need for suitable pangenomic tools to explore the genomic diversity within a species. We developed a new pipeline called panREPET that identifies TE insertions shared by groups of individuals. Unlike other pangenomic tools, panREPET operates independently of a reference genome and provides the precise sequence and genomic coordinates of each TE copy for each genome. We showcase the potential of this tool by identifying TE insertions shared among 42 Brachypodium distachyon genomes and by comparing our results with those of existing tools to demonstrate its advantages. Using panREPET, we were able to date two major TE bursts corresponding to major climate events: 22 kya during the Last Glacial Maximum and 10 kya during the Holocene, showing a potential link between environmental stress and TE activity.

Antifungal peptides (AFPs) are crucial for plant defense against biotic stress. Yet, no artificial intelligence tool specifically classifies plant AFPs. To fill this gap, we develop FungiGuard, which integrates Random Forest, Long Short-Term Memory, and attention mechanisms to identify AFPs using functionally annotated plant small peptides. FungiGuard outperforms existing generalized AFP model in classifying plant AFPs, and detects candidate AFPs in Arabidopsis, wheat, rice, and maize. It also discovers novel AFPs through randomly generated sequences. Experimental validation confirms the antifungal activity of candidate AFP against Botrytis cinerea. This tool deepens plant AFP understanding and facilitates novel AFP discovery.

Glucocorticoids are corticosteroid hormones that are commonly used for treating systemic inflammatory diseases and acute infections. Immunosuppressive effects of glucocorticoids have been studied in many cell types, particularly macrophages and T cells. Despite the importance and abundance of neutrophils in the human immune system, glucocorticoid responses remain understudied in neutrophils. Here, we perform quantitative mass spectrometry-based proteomics of primary neutrophils and neutrophil-like cells differentiated from human HL-60 promyelocyte cells. Primary neutrophils exhibited CK2 kinase activation and increase phosphorylation of HSP90 following 2-h incubation, highlighting potential effects of short-term ex vivo handling. Proteome and flow cytometry analysis show that neutrophil-like cells share features of neutrophils. Quantitative proteomics and phosphoproteomics of neutrophil-like cells treated with two synthetic glucocorticoid compounds, the clinical drugs dexamethasone and prednisolone, identify higher numbers of significantly regulated proteins and phosphosites compared to parental HL-60 cells. Glucocorticoid treatments modulated toll-like receptor signaling and CXCR4 serine phosphorylation. In addition, we identify RIPOR2 as a glucocorticoid-regulated protein associated with Rho GTPase signaling networks and actin cytoskeletal remodeling in neutrophils and neutrophil-like cells, though its exact functional role requires further investigation. Our results not only reveal unconventional regulatory mechanisms of glucocorticoids in the human immune system but also provide valuable resources for discovering novel glucocorticoid-responsive protein targets in neutrophils.

Dimensionality reduction is routinely applied to single-cell transcriptomic data to improve interpretability, remove noise and redundancy, and enable visualization. Most existing methods aim at preserving the most prominent data properties, which can lead to omission of rare but important signals. Here we propose a novel framework, SAKURA, that uses knowledge-derived genes of interest to guide dimensionality reduction, which can help cluster rare cells and separate highly similar cell subpopulations. We demonstrate the utility of our framework in identifying endocrine cell subtypes in the pancreatic islet, highly similar hematopoietic subpopulations, and rare senescent cells.

Drug combinations can improve cancer therapy by boosting efficacy, limiting dose-related toxicity, and delaying resistance. We present UniSyn, an interpretable multi-modal deep learning framework that transfers knowledge from monotherapy responses to enhance drug-synergy prediction. Through hybrid attention-based integration of drug and cell-line features, UniSyn supports multi-task learning and yields mechanistic insights. It generalizes robustly to unseen drug pairs and cell types, maintaining consistent performance across multiple synergy scoring metrics. Applied at scale to tumor cell lines, UniSyn captures context-specific synergy signals and prioritizes therapeutic combinations with translational potential.

BackgroundAging is a multi-modal process, leaving distinct molecular signatures across the epigenome. DNA methylation is among the most robust biomarkers of biological aging, yet most studies assume linear age relationships and analyze mixed-sex cohorts, overlooking known sex differences. Such approaches risk obscuring critical nonlinear transitions and sex-specific trajectories.ResultsWe develop SNITCH, a computational framework to detect complex nonlinear methylation trajectories and disentangle shared from sex-divergent patterns. Applied to the array-derived whole-blood methylomes from 252 females and 246 males (ages 19–90 years), SNITCH reveals convergent and divergent epigenetic aging pathways independent of immune cell composition. Nonlinear trajectories are enriched for developmental transcription factor motifs, including NF1/CTF and REST, with known oncogenic roles. Importantly, a female-specific nonlinear cluster is prospectively associated with cancer onset and systemic inflammation in an independent cohort, nominating clinically relevant biomarkers. We replicate the analysis in an additional cohort and highlight consistent nonlinear trajectories.ConclusionsOur results uncover sex-specific, nonlinear aging programs that capture the dynamics of epigenetic change beyond linear models. These findings provide potential candidate biomarkers for early disease risk and advance understanding of how aging trajectories diverge between sexes.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13059-026-03952-z.

Potato (Solanum tuberosum) breeding is severely hindered by its highly heterozygous autotetraploid genome, where complex allelic interactions impede precise trait selection. Reconstructing complete haplotype-resolved assemblies is crucial for genome-assisted breeding. However, current assembly methods for autopolyploids often generate fragmented sequences, haplotype-switch errors, and gaps in complex regions such as centromeres. To address these challenges, we develop PHap, a haplotype assembly pipeline tailored for autopolyploids, using only standard sequencing data, including long-reads and Hi-C. Applying PHap to the autotetraploid potato cultivar HuaShu4, we generate a haplotype-resolved, near telomere-to-telomere assembly of 3.12Gb with an N50 of 32.7Mb and 99.7% haplotype accuracy. Comparisons with alternative methods and existing assemblies highlight PHap's advantages in assembly quality and cost-effectiveness. Integration of transcriptomic and epigenomic data demonstrates that the genomic and methylation divergence across haplotypes drives substantial allelic expression differentiation. Time-course RNA-seq further reveals, for the first time, that 55% of genes exhibit divergent allelic expression, with dynamic shifts in dominant or suppressed alleles during tuber development. Additionally, our assembly resolves high-resolution haplotype-specific structures in centromeres and subtelomeres, as well as haplotype divergence of structural rearrangements. It also shows neocentromere formation via the expansion of megabase-scale satellite arrays. These findings provide insights into the architecture of autopolyploid genomes and establish a foundation for genomics-assisted breeding of polyploid potatoes.