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At least 37 records · Page 2Linked to original sources
PubMed2026

Genomic Characterisation of Carbapenem-Resistant Klebsiella pneumoniae and Enterobacter hormaechei Clinical Isolates from Nigeria: Evidence of Resistance, Virulence, and Putative Plasmid-Mediated Gene Sharing.

The global proliferation of carbapenem-resistant Enterobacterales (CRE) constitutes one of the most urgent public health threats, yet high-resolution genomic data from sub-Saharan Africa remain critically scarce. We applied whole-genome sequencing (WGS) and comparative phylogenomics to characterise antimicrobial resistance determinants, virulence genes, and mobile genetic elements (MGEs) in three carbapenem-resistant clinical isolates originating from three tertiary hospitals (selected from a broader surveillance collection spanning four facilities) in Osun State, southwestern Nigeria. We purposively selected three isolates, two Klebsiella pneumoniae subsp. pneumoniae (K22, ST411; K31, ST17) and one Enterobacter hormaechei subsp. steigerwaltii (K32, ST45) from a broader surveillance collection of 27 carbapenem-non-susceptible Enterobacterales, to represent phenotypically and genotypically divergent lineages. Resistome analysis revealed extensive plasmid-associated β-lactam and aminoglycoside resistance in K31 (including blaCTX-M-15, blaOXA-1, and blaTEM-1). K32 harboured an intrinsic chromosomal blaACT-17 AmpC gene, while IS26 and ISEcp1 insertion sequences, consistent with transposon-mediated mobilisation, flanked its acquired aminoglycoside and sulfonamide resistance cassettes. K22 lacked detected acquired carbapenemase, ESBL, or plasmid-mediated AmpC genes, indicating that its carbapenem-resistant phenotype may involve non-carbapenemase mechanisms such as porin alteration or efflux-mediated reduced susceptibility; however, this mechanism requires confirmation by direct ompK35/ompK36 sequence analysis and/or phenotypic outer membrane protein profiling. Virulome profiling identified a broader repertoire of siderophore, adhesion, and biofilm genes in both K. pneumoniae isolates than in E. hormaechei. Phylogenomic analysis demonstrated that K22 and K31 cluster within the broader K. pneumoniae population framework but represent distinct high-risk lineages (ST411 and ST17) rather than a single clonal outbreak. Analysis also identified a shared plasmid backbone between K31 and K32, supporting interspecies horizontal gene transfer. These descriptive genomic findings identify clinically relevant resistance and virulence determinants in three purposively selected carbapenem-resistant Enterobacterales from Nigerian tertiary-care hospitals. The detection of shared resistance elements between K. pneumoniae and E. hormaechei suggests possible plasmid-mediated gene sharing. Still, larger WGS studies with long-read sequencing and patient-level epidemiological data are required to define transmission and dissemination patterns.

Nigeria↗
PubMed2016

Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas.

Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology.

Atlases as Topic↗
PubMed2026

highSpaClone enables copy number alteration inference and tumor subclone analysis for high-resolution spatial transcriptomics.

High-resolution spatially resolved transcriptomics (SRT) offers unprecedented opportunities to investigate tumor heterogeneity but poses substantial computational and analytical challenges. Here, we present highSpaClone, a computational framework for copy number alteration (CNA) inference and tumor subclone identification from high-resolution SRT data across multiple spatial scales. By integrating spatial constraints into CNA estimation and clonal clustering, highSpaClone enables neighboring spatial locations to share information, thereby improving the robustness of genomic signals and the accuracy of subclone delineation. Across multiple Xenium and Visium HD datasets, highSpaClone revealed unique transcriptional programs, clonal evolutionary trajectories, and distinct tumor-microenvironment interactions. Furthermore, in human colorectal cancer samples, highSpaClone detected CNA events in histologically normal epithelial regions, highlighting early genomic alterations associated with field cancerization. These findings establish highSpaClone as a scalable framework for studying clonal architecture and tumor evolution.

CP: cancer biology↗
PubMed2026

Human and mouse adrenal glands are characterized by species-specific steroidogenic states and tissue turnover.

The adult adrenal cortex undergoes constant renewal, yet underlying human-specific mechanisms remain poorly understood. Here we generated single-cell and spatial transcriptomic atlases of adult human and mouse adrenal glands, leveraging single-cell-resolution spatial data and a rare clonal mosaic case for lineage inference. In humans, we identified age-associated zona glomerulosa (ZG) cell states with direct cortisol synthesis capacity and sex-specific differences in inferred cholesterol balance. Cross-species comparison revealed conserved aldosterone-producing ZG but notable divergence in zona fasciculata markers, absence of zona reticularis homologs in mice and differential SHH-WNT4 signaling in proliferating cells. We uncovered human WT1- capsule-to-ZG transition and vascular smooth muscle cell-to-steroidogenic transitions supported by mosaic lineage evidence. We revealed dispersed proliferating cortical SF1+EZH2+ cells throughout the human cortex in contrast with ZG restriction in mice. Taken together, our data expand the centripetal renewal model and establish a comparative framework for human adrenocortical biology.

Animals↗
PubMed2025

Genome wide association study of rice agronomical traits and seed ionome with the NARO Open Rice Collection.

To meet the nutritional needs of the rising human population, genetic variants are necessary for the breeding of new cultivars. Rice (Oryza sativa L.) is a staple food for over half of the world's population. Here, we developed a new rice genetic resource, the NARO Open Rice Collection (NRC) with high-resolution genome data. NRC consists of 623 accessions, and approximately 200 accessions are categorized into three major subgroups, categorized as Indica, Japonica, and Aus. In this study, we performed genome-wide association studies (GWAS) for rice heading date, seed shape, and seed ionome using the NRC. Well-known genes related to heading date and seed shape were detected by GWAS using the NRC accessions. Therefore, we concluded that our new rice collection is suitable for GWAS. In addition, GWAS with each subgroup was advantageous for the detection of particular genes. Finally, we performed GWAS for seed ionome with the aim of improving the nutritional properties of rice, as essential minerals for humans, such as iron (Fe) and zinc (Zn), are not sufficient in rice seeds. Our study revealed that OsATL31, a likely ubiquitin E3 ligase, was involved in the control of Fe and Zn contents in seeds.

Oryza↗
PubMed1977

Analog processing of vestibular nystagmus for on-line cross- correlation data analysis.

An analog processing circuit is described which allow accurate measurement of the phase relationships between input angular acceleration and resulting eye velocity. Vestibular nystagmic data are processed via analog technics to yield slowphase eye velocity. The turntable velocity input is cross-correlated with the eye velocity output, using a Nicolet MED-80 minicomputer system. The resulting correlograms are further processed to obtain precise phase information. Test data analysis shows a system resolution within 1 degree. Data from human and animal subjects are portrayed.

Acceleration↗
PubMed2025

DiCARN-DNase: enhancing cell-to-cell Hi-C resolution using dilated cascading ResNet with self-attention and DNase-seq chromatin accessibility data.

MOTIVATION: The spatial organization of chromatin is fundamental to gene regulation and essential for proper cellular function. The Hi-C technique remains the leading method for unraveling 3D genome structures, but the limited availability of high-resolution (HR) Hi-C data poses significant challenges for comprehensive analysis. Deep learning models have been developed to predict HR Hi-C data from low-resolution counterparts. Early Convolutional Neural Network (CNN)-based models improved resolution but struggled with issues like blurring and capturing fine details. In contrast, Generative Adversarial Network (GAN)-based methods encountered difficulties in maintaining diversity and generalization. Additionally, most existing algorithms perform poorly in cross-cell line generalization, where a model trained on one cell type is used to enhance HR data in another cell type. RESULTS: In this work, we propose Dilated Cascading Residual Network (DiCARN) to overcome these challenges and improve Hi-C data resolution. DiCARN leverages dilated convolutions and cascading residuals to capture a broader context while preserving fine-grained genomic interactions. Additionally, we incorporate DNase-seq data into our model, providing a robust framework that demonstrates superior generalizability across cell lines in HR Hi-C data reconstruction. AVAILABILITY AND IMPLEMENTATION: DiCARN is publicly available at https://github.com/OluwadareLab/DiCARN.

Chromatin↗
PubMed2026

Plant species identification by genome skimming across the vascular plant tree of life.

Accurate species identification is essential for biodiversity conservation and sustainable use, yet standard plant DNA barcoding often fails to achieve species-level resolution. We present a large-scale empirical evaluation of genome skimming as a tool to improve plant species discrimination. Using standardised data from 1969 individuals representing 475 species from 32 genera across major lineages of the vascular plant tree of life, we compare conventional plastid + internal transcribed spacer (ITS) barcodes with genome skimming approaches. Standard barcoding using rbcL, matK, trnH-psbA and ITS resolved about half of species (49.3%), with six genera showing <&#x2009;25% species discrimination. By contrast, genome skimming enabled the recovery of complete plastid genomes, yielding 57.6% species discrimination. It also generated sufficient nuclear genomic data for additional resolution from k-mer analysis, achieving 66.8% species discrimination - an average gain of 17.5% over standard barcodes - while eliminating cases of extreme failure (<&#x2009;25% resolution). The recovery of complete plastomes and ribosomal DNAs from genome skims also ensures backward compatibility with existing barcode datasets. Our results demonstrate that genome skimming provides data that substantially improves species-level resolution across diverse plant lineages and offers a scalable, high-throughput approach for building comprehensive reference resources to support global biodiversity initiatives.

DNA Barcoding, Taxonomic↗
PubMed1979

Low resolution models of self-assembled histone fibers from X-ray diffraction studies.

X-ray diffraction data from self-assembled histone fibers are presented for three systems: H4, H3-H4, and the four core histones H2A, H2B, H3 and H4. These data have been obtained under conditions of high ionic strength and high protein concentration which are thought to promote histone conformation similar to that found in intact chromatin. The low angle equatorial scattering (R less than .05 A-1) is analysed, and, with additional constraints imposed by electron microscopy data, four low resolution fibrillar models are derived. Two features common to all the possible models are a maximum outer diameter of approximately 60 A and a subfibril diameter of approximately 25 A. It is the interference of the protein subfibrils across a central region of low electron density - a 10 A "hole" - which gives rise to the characteristic diffraction peak at 36 A. Possible relationships of the models of the histone fibers to the structure of the histone component of chromatin are suggested.

Histones↗
PubMed2026

Agentomics: an agentic system that autonomously develops novel state-of-the-art solutions for biomedical machine learning tasks.

MOTIVATION: Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack flexibility, while large language models (LLMs) struggle to consistently deliver reproducible machine learning codebases, and existing LLM Agent-powered solutions lag behind human-engineered ML models. RESULTS: Here, we introduce Agentomics, an autonomous LLM-powered agentic system for end-to-end ML experimentation. Given a biomedical dataset, Agentomics implements various ML modeling strategies, and produces a ready-to-use ML model. Agentomics introduces strict validation checkpoints for standard ML development steps, allowing gradual development on top of working code with defined interfaces and validated artifacts. Further, it offers native support for biomedical foundation models that can be leveraged during experimentation. The generic nature of Agentomics allows the user to create ML solutions for a large variety of datasets and use various LLMs. We evaluate Agentomics across 20 datasets from the domains of Protein Engineering, Drug Discovery, and Regulatory Genomics. When benchmarked against other agentic systems, Agentomics outperformed them in all tested domains. When benchmarked against human expert solutions, Agentomics generated novel state-of-the-art models for 11/20 established benchmark datasets. AVAILABILITY AND IMPLEMENTATION: Agentomics is implemented in Python. Source code and documentation are freely available at: https://github.com/BioGeMT/Agentomics-ML.

Machine Learning↗
PubMed2024

STAG2 loss in Ewing sarcoma alters enhancer-promoter contacts dependent and independent of EWS::FLI1.

Cohesin complexes carrying STAG1 or STAG2 organize the genome into chromatin loops. STAG2 loss-of-function mutations promote metastasis in Ewing sarcoma, a pediatric cancer driven by the fusion transcription factor EWS::FLI1. We integrated transcriptomic data from patients and cellular models to identify a STAG2-dependent gene signature associated with worse prognosis. Subsequent genomic profiling and high-resolution chromatin interaction data from Capture Hi-C indicated that cohesin-STAG2 facilitates communication between EWS::FLI1-bound long GGAA repeats, presumably acting as neoenhancers, and their target promoters. Changes in CTCF-dependent chromatin contacts involving signature genes, unrelated to EWS::FLI1 binding, were also identified. STAG1 is unable to compensate for STAG2 loss and chromatin-bound cohesin is severely decreased, while levels of the processivity factor NIPBL remain unchanged, likely affecting DNA looping dynamics. These results illuminate how STAG2 loss modifies the chromatin interactome of Ewing sarcoma cells and provide a list of potential biomarkers and therapeutic targets.

Sarcoma, Ewing↗
PubMed2026

Breed classification of Lao People's Democratic Republic (Lao PDR) and Thai native chickens using synchrotron radiation-based Fourier transform infrared spectroscopy and genotyping by sequencing.

Lao PDR harbors substantial genetic diversity in native chicken populations, representing an important resource for sustainable production and long-term food security. This study aimed to classify five Lao native chicken breeds-Ou, Black Bone, Horn Chou, Yolk, and Chae-and to discriminate them from a Thai native breed, Leung Hang Khao (LK), using integrative genotype-based approaches. Blood samples were collected from 50 LK and Lao native chickens (32 Ou, 10 Black Bone, 9 Horn Chou, 121 Yolk, and 41 Chae). Genomic DNA was extracted and analyzed using synchrotron radiation-based Fourier-transform infrared (SR-FTIR) spectroscopy to characterize biochemical composition, while genotyping-by-sequencing (GBS) was employed to identify genome-wide single nucleotide polymorphisms (SNPs). SR-FTIR analysis revealed highly significant differences among breeds in nucleotide-associated functional groups, including thymine, adenine, guanine, cytosine, as well as DNA backbone and deoxyribose components (P < 0.001). Multivariate analyses demonstrated that principal component analysis (PCA) of SR-FTIR spectra effectively discriminated chicken breeds, while hierarchical cluster analysis (HCA) further resolved them into two major clusters with distinct sub-clusters, reflecting variation in DNA biochemical composition. In contrast, GBS analysis identified 1484 common SNPs; however, PCA based on SNP data showed limited resolution in clearly separating breeds, despite revealing similar clustering trends. Overall, the results highlight the strong discriminatory power of SR-FTIR spectroscopy for rapid and effective classification of native chicken breeds at the molecular level, outperforming SNP-based differentiation under the current marker density. This study provides novel insights into the application of synchrotron-based spectroscopic techniques in poultry genetics and contributes valuable baseline information for the conservation and utilization of Lao native chicken genetic resources.

Breed classification↗
PubMed2025

Doblin: inferring dominant clonal lineages from high-resolution DNA barcoding time series.

MOTIVATION: The lineage dynamics and history of cells in a population reflect the interplay of evolutionary forces they experience, including mutation, drift, and selection. When the population is polyclonal, lineage dynamics also manifest the extent of clonal competition among co-existing mutational variants. If the population exists in a community of other species, the lineage dynamics could also reflect the population's ecological interaction with the rest of the community. Recent advances in high-resolution lineage tracking via DNA barcoding, coupled with next-generation sequencing of bacteria, yeast, and mammalian cells, allow for precise quantification of clonal dynamics in these organisms. RESULTS: In this work, we introduce Doblin, an R suite for identifying dominant barcode lineages based on high-resolution lineage tracking data. We first benchmarked Doblin's accuracy using lineage data from evolutionary simulations, showing that it recovers the clones' identity and relative fitness in the simulation. Next, we applied Doblin to analyze clonal dynamics in laboratory evolutions of Escherichia coli populations undergoing antibiotic treatment and in colonization experiments of the gut microbial community. Doblin's versatility allows it to be applied to lineage time-series data across different experimental setups. AVAILABILITY AND IMPLEMENTATION: Doblin is available on CRAN (https://CRAN.R-project.org/package=doblin) and Github (https://github.com/dagagf/doblin).

DNA Barcoding, Taxonomic↗
PubMed1979

Territrems, tremorgenic mycotoxins of Aspergillus terreus.

The tremorgenic mycotoxins isolated from Aspergillus terreus were given the trivial names territrem A and B instead of their previous designations of C1 and C2 respectively. High-resolution mass spectral data suggested the molecular formula of territrem A to be C28H30O9 and that of territrem B,C29H34O9. They were partially characterized by ultraviolet, infrared, proton magnetic resonance, and mass spectroscopy. The spectroscopic evidence indicated that their chemical structures were very similar. The procedures of purification were also revised for the complete separation of these two chemically related compounds.

Aspergillus↗
PubMed2026

Mannheimia haemolytica strain-level diversity in cattle populations.

High-resolution genomic characterization is essential for understanding diversity, pathogenicity, and transmission dynamics of bacterial pathogens. Mannheimia haemolytica (Mh) is the most consequential bacterial agent associated with bovine respiratory disease (BRD) in cattle, as a leading cause of morbidity, mortality, and antimicrobial use. Historically, BRD pathogens, including Mh, have been studied using culture or PCR approaches that provided limited ability to characterize fine-scale genomic variation across communities. Here, we evaluated target-enriched (TE) shotgun sequencing, a culture-independent method capable of strain-level resolution within metagenomic data, for detecting and characterizing Mh in comparison with qPCR and 16S rRNA gene sequencing. Nasal swabs (10 individual and 2 composited DNA samples per pen) and environmental samples (three ropes hung on pen rails and three water bowl swabs per pen) were collected from four pens in each of five distinct cattle populations. DNA was extracted for TE sequencing to identify Mh at both species and genomic sequence variant (GSV) levels, and to characterize antimicrobial resistance genes across the bacterial communities. qPCR was performed to quantify Mh genome copies, and 16S rRNA gene sequencing was used to assess the broader respiratory microbiome. TE sequencing identified Mh in 100% of TE-tested samples and classified multiple GSVs in all but 3 of 121 samples. GSV profiles clustered within housing groups and varied across cattle populations, indicating structured strain-level diversity. In contrast, Mannheimia spp. were detected in only 47.7% of samples by 16S rRNA sequencing. These findings demonstrate that TE sequencing enables sensitive, strain-level characterization of Mh in cattle and environmental samples and reveals substantial within-population genomic diversity not captured by conventional approaches.IMPORTANCETarget-enriched shotgun sequencing enabled sensitive, strain-level detection of Mannheimia haemolytica (Mh), revealing multiple co-circulating genomic sequence variants (GSVs) within and among cattle groups. This demonstrates greater genetic variability of Mh populations in beef cattle than has been previously recognized. The clustering of GSVs within housing groups, together with the overlap between respiratory and environmental samples, is consistent with the hypothesis that contagious transmission contributes to Mh ecology. These results highlight the potential utility of composite nasal swab and environmental samples for future studies evaluating relationships between Mh genomic variation and disease risk.

Animals↗
PubMed1979

Progress report of the TUDAB project for automated cancer cell detection.

Two methods for high resolution cell image data acquisition are applied routinely. Cells are either scanned by a computer controlled fast scanning microscope photometer (SMP) or a TV-camera. The software system for digital image analysis was completely revised and implemented on the PR 330 minicomputer. The system contains codes for primary cell data acquisition, segmentation of cells, cell feature extraction and statistical cell analysis. With this system, SMP and TV scanned cell data bases of PAP stained cells in vaginal smears, grouped into several classes, have been built up. Each data base contains 34 primary features and 20 feature combinations for each cell. A linear discriminant analysis is applied routinely for cell classification. The present state of the system and its operation are described, cell features and classification results are shown, and future steps for a prescreening strategy are discussed.

Computers↗
PubMed2026

How advances in chromosome conformation capture (3C) methods are reshaping our understanding of gene regulation in hematopoiesis.

The three-dimensional organization of the DNA within the nucleus plays a key role in regulating gene expression. Over the past two decades, advances in chromosome conformation capture (3C) technologies, in tandem with other methods, have shown that the genome forms a complex structure at multiple scales. Early studies identified large-scale structures such as chromosome territories, compartments and topologically associating domains (TADs). As the resolution of 3C techniques has improved, it has become possible to identify contacts between regulatory elements in detail and more recently, it has become possible to define intricate structures within cis-regulatory elements. In this chapter, we review the development of 3C-based methodologies and discuss the strengths and limitations of the different approaches. We examine how these technologies have refined our understanding of genome organization and gene regulation. Recent high-resolution studies reveal that chromatin architecture extends beyond classical domain structures to include nanoscale organization. Integration of 3C data with super-resolution imaging and molecular dynamics simulations supports a model in which genome folding is governed by the biophysical properties of chromatin.

Animals↗
PubMed2025

Spatial transcriptomics-aided localization for single-cell transcriptomics with STALocator.

Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at single-cell resolution but lack spatial information. Spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality of the spatial resolution or gene coverage is still much lower than the quality of the single-cell transcriptomics data. To this end, we develop a ST-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods. When applied to the human brain and squamous cell carcinoma data, STALocator could robustly reconstruct the relative spatial organization of critical cell populations. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for fluorescence in situ hybridization (FISH) and Xenium data, leading to the identification of more spatially variable genes and more biologically relevant Gene Ontology (GO) terms compared with the raw data. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗