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PubMed2025

WinPCA: a package for windowed principal component analysis.

SUMMARY: With chromosomal reference genomes and population-scale whole genome-sequencing becoming increasingly accessible, contemporary studies often include characterizations of the genomic landscape as it varies along chromosomes, commonly termed genome scans. While traditional summary statistics like FST and dXY between pre-assigned populations remain integral to characterizing the genomic divergence profile, PCA differs by providing single-sample resolution, thereby supporting the identification of polymorphic inversions, introgression and other types of divergent sequence that may not be fully aligned with global population structure. Here, we introduce WinPCA, a user-friendly package to compute, polarize and visualize genetic principal components in windows along the genome. To accommodate low-coverage whole genome-sequencing datasets, WinPCA can optionally make use of PCAngsd methods to compute principal components in a genotype likelihood framework. WinPCA accepts variant data in either VCF or BEAGLE format and can generate rich plots for interactive data exploration and downstream presentation. AVAILABILITY AND IMPLEMENTATION: WinPCA is implemented in Python and freely available at https://github.com/MoritzBlumer/winpca and https://doi.org/10.5281/zenodo.15614979.

Software↗

PubMed2026

Validation of a Turkish Translation of the Stress in Emergency Healthcare Professionals: The Stress Factors and Manifestations Scale.

AIM: The primary duties of emergency healthcare professionals (EHPs) are to provide emergency patient care to acutely ill and injured individuals. Due to the nature of their work, EHPs operate under constant stress, often requiring rapid decision-making, swift action, and the delivery of necessary medical care in life-or-death situations, sometimes under inadequately safe conditions. Therefore, the aim of this study is to determine the validity and reliability of the Emergency Healthcare Professional Stress Factors and Symptoms (SEHP:SFMS) Scale in Turkish for identifying stress factors and symptoms in emergency medical care professionals providing emergency patient care services. DESIGN: A methodological study design was used in this study. METHODS: The study was conducted with the participation of 211 EHPs from employees working in emergency care institutions affiliated with the Muğla Provincial Health Directorate between November 2023 and June 2024. Data were collected via a face-to-face survey. Data were analysed using Lawshe content validity ratio, Kaiser-Meyer-Olkin coefficient, Bartlett test, exploratory factor analysis, principal component analysis, Varimax factor rotation method, confirmatory factor analysis, Cronbach's α internal consistency coefficient, convergent validity, discriminant validity, test-retest, and Spearman correlation coefficient tests. RESULTS: The linguistic translation and cultural adaptation of the SEHP:SFMS showed strong performance. The scope validity index of the scale is 0.83. The item-total correlation values of the scale were found to be between 0.486 and 0.794, and the factor loadings were between 0.474 and 0.816. Confirmatory factor analysis fit indices: χ2 = 248.727; df = 101; n = 211; p = 0.000; χ2/df = 2.463; RMSEA = 0.083; CFI = 0.914, SRMR = 0.052, which was found to be compatible and acceptable with the proposed 3-factor model. The Cronbach's α reliability coefficient of the scale was 0.931, and the total variance was 61.97%. CONCLUSIONS: SEHP:SFMS is a valid and reliable tool to assess stress factors and symptoms of Turkish emergency healthcare professionals. Its use improves the quality of emergency care. PATIENT OR PUBLIC CONTRIBUTION: These study findings have been used to create a tool with Turkish validity and reliability that allows for the examination of stress factors among healthcare professionals working in emergency and critical services. Identifying and reducing stress factors among healthcare professionals is crucial for the delivery of quality healthcare services. It can also be used to develop targeted interventions and ongoing strategies to facilitate improved clinical supervision and mentoring. IMPLICATION FOR NURSING PRACTICE: Nurses in emergency departments, which are among the most stressful, dynamic, intense, life-saving, and critical environments in healthcare institutions, and where life-saving treatment is administered, are at high risk of experiencing psychological trauma. Trauma experienced in the work environment is a significant problem for nursing. The consequences of trauma negatively affect nurses and institutions. Studies show that post-traumatic stress, anxiety, depression, and burnout are commonly observed in emergency department nurses. In this sense, understanding the stress and stress factors experienced by nurses can guide future interventions. The results of this study are considered important in making visible the stress and stress factors experienced by nurses in the emergency department, and also in guiding managers and nurses working in this field in terms of preventive and protective measures.

Humans↗

PubMed2026

Genome-Wide SNP Characterisation of Three Kazakh Sheep Breeds: Kazakh Fat-Tailed Coarse-Wool, Degeres, and Etti Merino.

Kazakhstan's sheep portfolio underpins much of the country's mutton and wool production, yet several of its principal breeds remain genomically uncharacterised. The aim of this study was to characterise the genomic diversity, population structure, and global phylogenetic placement of three economically important Kazakh breeds and to determine whether they constitute separate gene pools requiring independent management. We present the first genome-wide SNP characterisation to include the Degeres (DE), the Etti Merino (EM), and the Kazakh fat-tailed coarse-wool (KKG) breeds simultaneously. A total of 1497 animals (DE = 354, EM = 642, KKG = 501) sampled across seven production households were genotyped and, after quality control, analysed at 42,279 SNPs, of which 22,766 LD-pruned markers were used for principal component analysis and AMOVA. We applied principal component analysis (PCA), pairwise FST, analysis of molecular variance (AMOVA), neighbour-joining phylogenetics, model-based ancestry estimation (ADMIXTURE), and Hill-number diversity profiling, and projected the breeds against the global Ovine SNP50 HapMap panel (74 reference breeds, 2819 animals; 37,685 shared SNPs). All three breeds retained uniformly high within-breed diversity (expected heterozygosity 0.413-0.417) with fixation indices at or near zero. AMOVA partitioned 94.03% of variance within breeds (ΦST = 0.060, p < 0.001). PCA, phylogeny, and ADMIXTURE concordantly resolved three breed-specific clusters at K = 3, with a maximum interbreed FST of 0.038 within the study dataset. Against the global panel, EM was genetically closest to Merino and Merino-derived reference breeds (pooled FST = 0.017) and substantially more distant from Southwest Asian sheep (FST = 0.045), whereas DE and KKG showed the reciprocal pattern (FST = 0.027 and 0.020 to Southwest Asia, 0.052 to the Merino group). DE additionally displayed the heterozygote excess and partial admixture expected of an incompletely consolidated composite. These results delineate three distinct gene pools and carry direct implications for breed management and the conservation of genomic diversity in Kazakhstani sheep.

ADMIXTURE↗

PubMed2026

slideimp: efficient imputation of DNA methylation data.

SUMMARY: We developed slideimp, an R package that extends and optimizes K-nearest neighbor (K-NN) and Principal Component Analysis (PCA) imputation with grouped and sliding-window modes for accurate and efficient imputation of microarray and whole-genome DNA methylation (DNAm) data, respectively. Under a realistic scenario, slideimp achieved ≈12-28× faster runtime and ≈3-6× peak memory usage reduction for DNAm microarray imputation (GSE286313, EPICv2, N = 72) and achieved high imputation accuracy in a whole-genome DNAm dataset (N = 41). AVAILABILITY AND IMPLEMENTATION: The code used in this study is available at https://github.com/hhp94/slideimp_paper. The R package slideimp is available on CRAN (DOI: 10.32614/CRAN.package.slideimp). Version 1.0.0 of slideimp, which was used in this study, is archived on Zenodo (DOI: 10.5281/zenodo.20029382).

DNA Methylation↗

PubMed2025

Use of IR Biotyper as a feasible methodology to type Klebsiella pneumoniae.

UNLABELLED: Klebsiella pneumoniae is one of the most frequently reported healthcare-associated pathogens. The current gold standard approach to perform the epidemiological typing of these bacteria is Whole Genome Sequencing (WGS), which is an expensive and challenging procedure. IR Biotyper (Bruker Daltonics, GmbH) is a new equipment based on Fourier transform infrared spectroscopy, which allows a rapid, low-cost, and user-friendly method to type bacterial isolates. However, there is a need for studies that evaluate the efficacy of the IR Biotyper. The aim of this study was to evaluate the capability of IR Biotyper to type K. pneumoniae according to sequence type (ST) and capsular type-using K locus (KL)-as well as to develop a classifier using machine learning. Seventy-three isolates of K. pneumoniae previously characterized by WGS were selected for IR Biotyper analysis using principal component analysis for dimensionality reduction, Euclidean, and unweighted pair group method with arithmetic mean (UPGMA) for clustering method, and spectra were analyzed in the 1,300-800 cm⁻¹ wavenumber range. Among these, 54 isolates were used to create a classifier, and 19 were used to validate the classifier. When considering the ST, ST307 was grouped in the same cluster as ST11. When KL was considered for the analysis, the clusters were 100% correctly grouped according to their KL type. Furthermore, the classifier developed was able to classify the isolates according to KL with a high concordance. This study showed that KL correlates well with KL for typing K. pneumoniae isolates using the IR Biotyper. Additionally, IR Biotyper demonstrated to be a cost-effective method and a promising tool to classify isolates within minutes. IMPORTANCE: Klebsiella pneumoniae is a major cause of severe hospital infections, and controlling its spread requires quick identification and comparison of bacterial strains. WGS is accurate but expensive, slow, and technically demanding. In this study, we evaluated the IR Biotyper, a device that uses infrared light to analyze bacteria and group them by capsule type-a key feature linked to their spread. The IR Biotyper matched WGS results with high accuracy, delivering results in minutes instead of days. This fast, affordable method can help hospitals detect outbreaks earlier and respond more effectively. Our findings suggest that the IR Biotyper is a valuable tool for routine use in microbiology laboratories, supporting epidemiological surveillance and outbreak control.

Klebsiella pneumoniae↗

PubMed2026

Microrna Expression in Aurelia aurita Metamorphosis.

INTRODUCTION: In animal taxa and jellyfish, the same genome encodes for the different phenotypes that characterize life stages that follow each other during ontogeny. This situation underscores the existence of profound regulation of genomic information at the epigenetic level. MicroRNAs are fundamental epigenetic regulators. The aim of this study is to evaluate the role of microRNA regulation during jellyfish metamorphosis and to explore the existence of evolutionarily conserved microRNAs. METHODS: Specimens belonging to the 4-metamorphosis stages of A. aurita (polyps, ephyra, young, and adult jellyfish) were bred and collected. The expression of 2,549 miRNAs for each stage was tested using microarray technology. The comparison of microRNA expression for each phase was performed using line plot analysis and Principal Component Analysis of variance (PCA), while the identification of microRNA clusters was performed via volcano plot analysis. RESULTS: A remarkable number of A. aurita miRNAs specifically hybridize with a human miRNA library. Each metamorphosis stage is characterized by a different level of expression of miRNAs: 1) Polyp vs. Ephyra stage: 128 upregulated, 2 downregulated; 2) Ephyra vs. Young stage: 2 upregulated, 135 downregulated; 3) Young vs. Adult stage: 69 upregulated, 6 downregulated. Specific functions inferred from known activities of corresponding miRNAs in higher animals (PubMed database) appear to be coherent with the correlated experimental model. DISCUSSION: Present results reveal that microRNAs with human homologs undergo specific expression changes throughout Aurelia aurita metamorphosis. This observation reinforces the hypothesis of a shared evolutionary origin of certain miRNA families between Cnidaria and Bilateria. The dynamic and stage-specific regulation pattern observed suggests that miRNAs play a key role in orchestrating the complex transitions involved in jellyfish development. These findings point to a broader conservation of epigenetic mechanisms, such as miRNA-mediated gene silencing, which may have emerged early in metazoan evolution and contributed to the regulation of cell differentiation and phenotype modulation. CONCLUSION: The present study highlights the importance of Aurelia aurita as a model for investigating miRNA-driven epigenetic regulation in non-bilaterian animals. The identification of human-homologous miRNAs provides novel insights into the evolutionary stability of the epigenetic machinery and suggests conserved regulatory functions across distant taxa. Although limited by the use of a human-based microarray platform, the data presented here lay a solid foundation for future studies employing sequencing and functional assays to further explore the role of miRNAs in cnidarian development and evolution.

Animals↗

PubMed2026

Integrated Metabolomic and Transcriptomic Analysis Reveals Tissue-Specific Secondary Metabolic Differentiation and Indole Alkaloid Accumulation in Evodia rutaecarpa.

Evodia rutaecarpa is a valuable medicinal plant, yet its non-medicinal tissues remain largely underexplored. Here, we integrated ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics and RNA sequencing (RNA-seq) transcriptomics to systematically profile the metabolic and transcriptional landscapes of roots, stems, leaves, and flowers of Evodia rutaecarpa (Juss.) Benth. Our aim was to characterize tissue-specific metabolic differentiation and its underlying transcriptional regulatory mechanisms. Metabolomic analysis, employing principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) with robust model parameters (R2Y > 0.9, Q2 > 0.5), identified 3090 differential metabolite features (variable importance in projection, VIP > 1.0; p < 0.05) across the four tissues, which exhibited distinct tissue-specific clustering patterns. Integrated Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and weighted gene co-expression network analysis (WGCNA) revealed that roots specifically accumulated quinolone alkaloids and flavonoid glycosides, accompanied by the coordinated upregulation of genes involved in flavonoid and phenylpropanoid biosynthetic pathways. In contrast, stems, leaves, and flowers were enriched in indole alkaloids (evodiamine and rutaecarpine) and volatile oil precursors, with concurrent upregulation of genes involved in tryptophan metabolism and indole alkaloid biosynthesis (e.g., tryptophan decarboxylase, TDC; s N-methyltransferase, NMT). Notably, leaves and flowers displayed particularly high accumulation levels of these bioactive alkaloids, suggesting their potential as alternative sources for industrial and pharmaceutical applications. WGCNA further identified multiple transcription factors and structural gene modules tightly correlated with evodiamine accumulation, offering promising candidate regulators for future biosynthetic pathway engineering. Collectively, this multi-omics integration study systematically elucidates the tissue-partitioned secondary metabolism of Evodia rutaecarpa (Juss.) Benth. and provides a solid scientific foundation for full-plant resource utilization, targeted development of non-medicinal tissues, and future metabolic engineering of indole alkaloid production.

Evodia rutaecarpa↗

PubMed2026

Morphological characterization, genetic diversity and population structure of the rice blast pathogen Magnaporthe oryzae in Northeast India.

The blast pathogen, Magnaporthe oryzae, is one of the most destructive fungal pathogens of rice worldwide, yet its morphological features, genetic diversity and population structure in Northeast India remain poorly understood. In this study, twenty‒two M. oryzae isolates collected from eight states of Northeast India were characterized using morphological, molecular, and population genetic analyses. Morphological characterization revealed whitish to greyish‒white mycelia with sparse sporulation and colony diameters ranged from 36 to 90 mm, classifying the isolates into 14 fast and 8 slow‒growing groups. Whole genome sequencing was performed to enable both ITS‒based identification and SSR locus mining from the assembled genomes. Molecular identification using ITS rDNA sequences confirmed all isolates as M. oryzae, with 95.5-100% similarity. Phylogenetic analysis grouped the isolates into two major clades and identified seven ITS sequence types (GenBank Accessions: PX273287-PX273293). Genetic diversity assessed using 30 SSR markers revealed substantial polymorphism, with 1-7 alleles per locus and polymorphism information content (PIC) values ranging from 0.00 to 0.81. Heatmap clustering, dendrogram analysis, and distance metrics consistently identified two major genetic groups, with some isolates forming nearly identical clusters and others showing moderate divergence. Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA) accounted for 87.8% of the total variance (PC1 and PC2 accounted for 54.4% and 33.4% respectively of the total variance) and revealed distinct outliers. Analysis of Molecular Variance (AMOVA) attributed 80% of the total genetic variation to differences among populations while only 20% was attributed to within population differences highlighting significant inter‒population divergence and clonal population structure. The study revealed substantial morphological and genetic diversity among M. oryzae populations in Northeast India, underscoring the need for region‒specific disease management strategies.

India↗

PubMed2026

Pilot study identifying distinct circulating proteomic profiles associated with longitudinal CT-defined fibrotic and inflammatory sarcoidosis.

INTRODUCTION: Pulmonary sarcoidosis exhibits heterogeneous clinical trajectories ranging from self-limited disease resolution to chronic progressive fibrosis, yet reliable biomarkers capable of distinguishing these disease patterns remain lacking. Whether longitudinal CT-defined sarcoidosis phenotypes are associated with distinct circulating molecular signatures remains unknown. METHODS: We performed high-throughput plasma proteomics (SomaScan 11K) in participants with pulmonary sarcoidosis classified into longitudinal chest CT-defined progressive fibrosis, progressive nodular inflammatory disease, or resolving disease trajectories, along with healthy controls. CT phenotypes were assigned based on predefined longitudinal changes in reticulation, traction bronchiectasis, nodular involvement, and mediastinal lymphadenopathy across serial CT scans. One plasma sample per participant was selected from the study visit corresponding to the CT time point at which criteria for the assigned longitudinal phenotype were met. Principal component analysis, hierarchical clustering, pathway enrichment, and correlation-based analyses linking protein expression to quantitative CT features were used to evaluate whether distinct longitudinal CT phenotypes were associated with divergent proteomic signatures. RESULTS: Principal component analysis and hierarchical clustering suggested partial segregation by CT-defined phenotype. Longitudinal CT phenotypes were associated with distinct pathway-level proteomic signatures, with progressive fibrosis enriched for epithelial-mesenchymal transition signaling, and progressive nodular inflammatory disease enriched for mTORC1, MYC, oxidative phosphorylation, adipogenesis, and fatty acid metabolism pathways. Correlation analyses showed coordinated protein-expression patterns associated with fibrotic CT features and mediastinal lymph node enlargement. DISCUSSION: These findings suggest that longitudinal CT-defined fibrotic and inflammatory sarcoidosis phenotypes are associated with distinct pathway-level proteomic signatures. This pilot study provides preliminary proof-of-concept evidence that integrating longitudinal CT imaging phenotypes with plasma proteomics may serve as a framework for future mechanistic studies and biomarker discovery in pulmonary sarcoidosis.

Humans↗

PubMed2026

Research on identification of key genes and immune-metabolic mechanisms in atrial fibrillation through integrated multi-cohort transcriptomic analysis and machine learning.

This study aimed to integrate multiple datasets for the identification of atrial fibrillation (AF)-related differentially expressed genes (DEGs), analyze their underlying mechanisms through functional enrichment and machine learning, construct diagnostic models, and explore immune-metabolic interactions to provide novel biomarkers and theoretical foundations. Gene expression datasets were integrated and normalized, with batch effects removed using principal component analysis. Differential expression analysis, functional enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways), and machine learning-based feature gene selection and model construction were performed. Shapley additive explanations analysis was utilized to interpret the constructed models, while gene set enrichment analysis, gene set variation analysis, and immune cell infiltration analysis were conducted to investigate the associations between feature genes and immune infiltration. After integrating and normalizing gene expression data and eliminating batch effects via principal component analysis, 6 DEGs were identified, including 4 upregulated and 2 down-regulated ones. Functional enrichment analysis showed these DEGs were significantly enriched in neuro-related biological processes and pathways, indicating their key roles in AF pathogenesis. Five key feature genes were selected using LASSO, random forest, and support vector machine-recursive feature elimination algorithms. They had significant expression differences between the AF and control groups (P < .001) and were located on distinct chromosomes. The constructed random forest and support vector machine models performed excellently (area under the curve ≥ 0.85). Shapley additive explanations analysis revealed TNNI1 contributed most to model prediction, with its expression significantly positively correlated with immune cell infiltration. Gene set enrichment analysis and gene set variation analysis analyses further showed feature genes participated in AF pathogenesis by regulating immune modulation, metabolic pathways, and autophagy. Immune cell infiltration analysis found altered proportions of T-cell subsets and M0 macrophages in the AF group, along with complex links between feature gene expression and immune cell function. This study systematically elucidated the unique gene expression patterns and key regulatory pathways associated with AF, clarifying the crucial roles of feature genes in immune regulation, metabolic imbalance, and cellular dysfunction. These findings provide a theoretical basis and potential therapeutic targets for understanding AF pathogenesis and developing targeted treatment strategies.

Atrial Fibrillation↗

PubMed2025

Charting the phenotypic landscape of mitochondrial diseases through a systematic evaluation of pathogenic mitochondrial DNA and nuclear gene variants.

PURPOSE: Primary mitochondrial diseases (PMD) arise from variants in the mitochondrial or nuclear genomes. Phenotype-based recognition of specific PMD genotypes remains difficult, prolonging the diagnostic odyssey. We expanded the MitoPhen database to characterize phenotypic variation across PMD more systematically. METHODS: Individual-level data on mitochondrial DNA disorders, nuclear-encoded mitochondrial diseases, and single large-scale mitochondrial DNA deletions were manually curated with Human Phenotype Ontology (HPO) terms to produce MitoPhen v2. Principal-component analysis summarized system-level abnormalities; HPO-level enrichment and mean phenotype-similarity scores were then used to distinguish common PMD genotypes. RESULTS: MitoPhen v2 adds 3940 individuals to the original release, now encompassing 1597 publications, 10,626 individuals, and 117 genotypes. Among 7586 affected cases, 72,861 HPO terms were recorded. Principal-component analysis revealed 6 phenotype dimensions capturing most system-level variance. At the HPO level, we observed genotype-specific enrichments and identified 111 gene-phenotype links absent from the current HPO database. Using MT-TL1, single large-scale mitochondrial DNA deletions, and POLG as exemplars, phenotype-similarity scores reliably separated individuals with these genotypes from those without. CONCLUSION: MitoPhen v2 enabled systematic, genotype-aware analysis of heterogeneous PMD phenotypes and highlighted the diagnostic value of structured, individual-level data. Phenotype-similarity metrics from such data sets can refine variant interpretation in large rare-disease cohorts and provide a transferable framework for other phenotypically complex genetic disorders.

Humans↗

PubMed2026

[Genetic diversity analysis of Forsythia suspensa germplasm resources in Shanxi based on phenotypic traits and SNP molecular markers].

This study aimed to clarify the degree of fruit phenotypic variation and the characteristics of genetic diversity, population structure, and genetic differentiation of Forsythia suspensa resources in Shanxi, providing an important basis for germplasm conservation and breeding of superior varieties. A total of 46 F. suspensa fruits were collected, and 12 agronomic traits were measured and analyzed. The population genetic structure and genetic diversity of F. suspensa germplasm were evaluated using simplified genome sequencing technology. For the five quality traits of the 46 fruits, the Shannon-Wiener index ranged from 0.631 to 1.074, and the Simpson index ranged from 0.379 to 0.560. The seven quantitative traits exhibited abundant genetic variation, with coefficients of variation ranging from 9.764%(fruit shape index) to 45.494%(forsythin content). Principal component analysis reduced the 12 phenotypic traits to four factors, with a cumulative variance contribution of 74.547%. Sequencing data showed mean Q20 and Q30 values of 98.13% and 94.33%, respectively, with an average GC content of 35.95%. After filtering, a total of 12 347 327 high-quality single nucleotide polymorphism(SNP) loci were obtained. Based on these high-quality SNPs, principal component analysis, population structure analysis, and phylogenetic tree construction were carried out. The 46 germplasm resources were divided into four groups; however, grouping showed little relationship with geographic origin, and intermixing occurred among regions. Mantel test revealed a significant but weak positive correlation between phenotypic and genetic distances(r=0.159, P=0.001). At the molecular level, the four groups exhibited moderate genetic diversity overall, and the genetic differentiation index among populations ranged from 0.027 to 0.084, indicating low to moderate differentiation. The rich genetic diversity of the main phenotypic traits provides a solid material basis for screening superior germplasm and genetic breeding of F. suspensa.

Forsythia↗

PubMed2025

MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures.

The genome is organized into distinct chromatin compartments with at least two main classes, a transcriptionally active A and an inactive B compartment, broadly corresponding to euchromatin and heterochromatin. Chromatin regions within the same compartment preferentially interact with each other over regions in the opposite compartment. A/B compartments are traditionally identified from ensemble Hi-C contact frequency matrices using principal component analysis of their covariance matrices. However, defining compartments at the single-cell level from sparse single-cell Hi-C data is challenging, especially since homologous copies are often not resolved. To address this, we present MaxComp, an unsupervised method, for inferring single-cell A/B compartments based on 3D geometric considerations in single-cell chromosome structures-derived either from multiplexed FISH-omics imaging or 3D structure models derived from Hi-C data. By representing each 3D chromosome structure as an undirected graph with edge-weights encoding structural information, MaxComp reformulates compartment prediction as a variant of the Max-cut problem, solved using semidefinite graph programming (SPD) to optimally partition the graph into two structural compartments. Our results show that the population average of MaxComp single-cell compartment annotations closely matches those derived from ensemble Hi-C principal component analysis, demonstrating that compartmentalization can be recovered from geometric principles alone, using only the 3D coordinates and nuclear microenvironment of chromatin regions. Our approach reveals widespread cell-to-cell variability in compartment organization, with substantial heterogeneity across genomic loci. When applied to multiplexed FISH imaging data, MaxComp also uncovers relationships between compartment annotations and transcriptional activity at the single-cell level. In summary, MaxComp offers a new framework for understanding chromatin compartmentalization in single cells, connecting 3D genome architecture, and transcriptional activity with the cell-to-cell variations of chromatin compartments.

Chromatin↗

PubMed2026

Intraskeletal Variation in Cortical Bone Quantity in a Medieval Italian Sample: A Multivariate Exploratory Approach.

Bioarcheologists interpret skeletal health by examining variability within and between individuals. Studies of bone loss have generated contradictory and conflicting results regarding the onset and severity of age-related bone loss on a global and temporal scale, perhaps due to mismatched methodologies. Intraskeletal comparisons of bone tissue prove challenging precisely because of heterogeneous baselines in quantity and remodeling of cortical bone throughout the skeleton, as well as evolutionary histories and environmental impacts on growth and development. Here we analyze cortical bone indicators from the rib, metacarpal, and femoral cortical bone in a subset of individuals (n = 72) regions from the medieval Italian archaeological site of Pieve di Pava. To facilitate intraskeletal comparisons across elements with different biological baselines, we standardize cortical bone parameters using z-scores. Variation in relative intraskeletal cortical bone was assessed using accessible multivariate methods (principal component analysis and hierarchical cluster analysis). Results suggest an association between femoral and metacarpal cortical bone values, with stochastic trends in metacarpal and femoral relative bone quantity in relation to the rib bone quantity at the sample level. Our study demonstrates that while intraskeletal analyses are challenging, they are made more robust by synthesizing multivariate methods alongside exploratory data analysis (EDA) methods to tack between sample-level and individual-level scales and variability. Ultimately, we advocate for leveraging multivariate techniques not as a final step, but rather as a means of generating new hypotheses and challenging tendencies to a priori establish typological groups in the research process.

Skeleton↗

PubMed2026

Genomic diversity, inbreeding, and selection signatures in duroc, landrace, and yorkshire pigs from a long-term closed breeding system.

Duroc (DD), Landrace (LL), and Yorkshire (YY) are among the most widely used commercial pig breeds, having undergone intense long-term selection within closed breeding systems. This study presents a comprehensive genomic analysis of genetic diversity, inbreeding patterns, and selection signatures in DD, LL, and YY populations that have been subject to close breeding for over 15 years. Genomic and pedigree data were available for 1,088 animals (DD = 348, LL = 276, YY = 464), genotyped using the GenoBaits® Porcine 100 K SNP panel. Principal component analysis and genetic diversity metrics revealed distinct population structures among the three breeds. Pairwise genetic differentiation supported this pattern, with DD showing the greatest divergence from LL (0.34 ± 0.24) and YY (0.33 ± 0.24), while LL and YY were more closely related (FST = 0.22 ± 0.19). Linkage disequilibrium (LD) analysis further confirmed these differences, as DD exhibited the highest average r² (0.34), followed by LL (0.28) and YY (0.25). Within-breed genetic diversity metrics, including observed heterozygosity (HO: 0.37 in DD, 0.39 in LL, 0.38 in YY), expected heterozygosity (HE: 0.36 in DD, 0.37 in LL, 0.38 in YY), and minor allele frequency (MAF: 0.27 in DD, 0.28 in LL, 0.29 in YY), indicated greater genetic variability in LL and YY compared to DD. Runs of homozygosity (ROH) analyses revealed different patterns of autozygosity, with DD exhibiting more long ROH indicative of recent inbreeding, while YY harbored a higher number of short ROH, suggestive of more ancient demographic events. ROH-based inbreeding coefficients (FROH) consistently exceeded pedigree-based estimates (FPED) across all breeds, highlighting the presence of recent or unrecorded inbreeding that pedigree data may not fully capture. According to Generation Proxy Selection Mapping (GPSM), 17, 1, and 12 significant SNPs were detected in DD, LL, and YY, respectively. Functional annotation of ROH islands and GPSM-significant loci revealed both breed-specific and overlapping QTLs related to traits such as growth, reproduction, and carcass. In general, the findings of this study contribute to a deeper understanding of the genomic consequences of long-term closed breeding and provide reference information to support consideration of breeding strategies that balance continued selection for productivity with the maintenance of genetic diversity in modern commercial pig populations.

Animals↗

PubMed2026

Genome-wide SNP-based genomic diversity and population structure analysis in alpaca populations from Europe and Peru.

This study aimed to analyze the genetic diversity and population structure of alpacas in Germany, Switzerland, and Austria (German-speaking regions, GSR) and to compare with that of the country of origin of the species (Peru). A total of 179 animals from GSR and 151 from Peru were genotyped with a species-specific 76k SNP array. The observed and expected heterozygosity was 0.305 and 0.311 for GSR and 0.310 and 0.312 for Peru. The mean FROH values were 0.029 for GSR and 0.023 for Peru. In general, results show that breeders in both analyzed regions efficiently maintain genetic diversity. Principal component analysis identified the GSR and Peru populations as separate from each other, but the relative proximity of both clusters indicates the shared genetic heritage. FST and XPEHH methods identified genomic regions under selection for traits such as coat color and adaptation. Genome-wide association studies comparing black and brown with white or gray alpacas identified associated genome regions containing the ASIP and KIT genes, respectively. The association of a recently identified keratin locus on chromosome 16 with differences in fleece type in alpacas was confirmed, while the putative causality of a TRPV3 variant was rejected.

Animals↗

PubMed2026

Urine Proteomics as a Source of Biological Information and Outcome Predictor in Living Kidney Transplantation.

Kidney transplantation (KTx) is the preferred treatment for kidney failure. However, post-transplant management is challenging due to the limited lifespan of transplanted organs. Current methods for monitoring post-transplant complications are invasive and have limitations. Therefore, there is an urgent need for novel non-invasive biomarkers. This study investigates the proteomic composition of urine to understand renal biology during the process of transplantation and to identify potential markers for outcome prediction. Urine samples were collected from donors before transplantation and from recipients 4 weeks and 1 year after transplantation. Proteomic analysis was performed using mass spectrometry and label-free quantification. Statistical analyses included principal component analysis (PCA) and enrichment analysis. The resulting key findings were confirmed in an independent validation cohort. In addition, correlative regression models to evaluate the relationship between protein abundance and clinical outcomes in the further course after transplantation were performed. 106 urine samples in the setting of 70 kidney transplantations were analyzed. PCA revealed distinct clustering of donor and recipient samples, indicating significant proteomic changes after transplantation. Hierarchical clustering and gene ontology analysis identified molecular changes as a response to transplantation and showed an over-representation of relevant pathways related to inflammation, cell immune response and coagulation in both the original and validation cohorts. Multivariate regression analysis, including linear and logistic regression, identified 11 potential protein biomarkers, including ORM2, IL1RAP, APP, and FABP4 as predictors of eGFR 12 months after transplantation and 1 HP as a predictor of infections within the first year after transplantation, respectively. This study underscores the potential of non-invasive urine proteomics for identifying biological processes involved in kidney transplantation and for enhancing post-transplant monitoring and outcome prediction. We identified 12 potential biomarkers with added value to standard clinical parameters linked to transplant outcomes, which will be promising candidates for future outcome monitoring after KTx.

Humans↗

PubMed2026

Genomic analysis of breed composition and population structure in Montana composite cattle.

The Montana composite was developed in Brazil from crosses between Bos indicus and Bos taurus and structured into four biological types: Zebu (N), adapted taurine (A), British taurine (B), and continental taurine (C). This study aimed to characterize the genetic diversity and population structure of the Montana composite using genomic data through principal component analysis (PCA), admixture analysis, and Wright's FST statistic. The PCA revealed a clear separation between Bos indicus and Bos taurus groups, with Montana animals distributed in an intermediate position. The first two principal components explained 69.48% and 3.45% of the total variation, respectively. Supervised admixture estimates indicated a predominance of taurine contribution, with type A accounting for 34.47%, 52.64%, and 51.71% at K = 4, 9, and 11, respectively. Increasing the ancestry resolution refined the contribution of individual founder breeds without changing the overall predominance of taurine ancestry. Comparisons between breed proportions obtained from pedigree and genomic data revealed significant differences, for most biological types and ancestry models (P < 0.001), indicating that realized breed composition deviates from theoretical expectations. Estimates of genetic differentiation confirmed greater divergence between Zebu and taurine groups, as well as reduced distances among populations sharing common ancestry. Specific relationships were identified between the composite and some of its founder breeds, particularly Belmont Red, Senepol, and Tuli. Overall, the results demonstrate that the Montana composite has a complex genomic structure, with genomic ancestry varying according to the resolution adopted and differing from pedigree-based expectations.

Animals↗