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PubMed2026

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.

SUMMARY: Image-based spatial transcriptomics (iST) deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in (iST) dataset, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. This highly scalable tool comprehensively segments tissue into spatial domains, aiding in biological interpretation of iST data and its underlying molecular microenvironments. AVAILABILITY AND IMPLEMENTATION: The SpatialRNA package is freely accessible from online repository https://github.com/ruqianl/spatialrna and can be installed via pip. Comprehensive tutorials, guidance on parameter selection, and complete workflows of case studies are available from the documentation website https://ruqianl.github.io/spatialrna_docs/, and uploaded on Zenodo with a DOI 10.5281/zenodo.17339575.

Neural Networks, Computer↗

PubMed2026

stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images.

MOTIVATION: Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP technologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. RESULTS: Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis. AVAILABILITY AND IMPLEMENTATION: The source code of stDyer-image and detailed tutorials are available at https://github.com/ericcombiolab/stDyer-image.

Proteomics↗

PubMed2026

Identification of immune cell type-specific susceptibility genes in multiple cancers using transcriptome-wide association studies.

BACKGROUND: Transcriptome-wide association studies (TWAS) integrate gene expression and genome-wide association studies (GWAS) to identify disease susceptibility genes. Because gene expression varies substantially across cell types within tissues, cell type-specific prediction models may enhance the power of TWAS. METHODS: We conducted cell type-specific TWAS leveraging single-cell RNA sequencing data from the OneK1K cohort (14 immune cell types, 1.27 million cells) and GWAS summary statistics for 7 cancers (>290 000 cases in total). To improve prediction accuracy, we developed a modeling framework that incorporates shared gene expression effects across cell types. RESULTS: At a false discovery rate of 5%, we identified 106 (Bonferroni 5%: 13) previously unreported loci for breast cancer, 51 (4) loci for prostate cancer, 11 (4) loci for lung cancer, 39 (5) loci for melanoma, 9 (1) loci for ovarian cancer, and 2 (1) loci for diffuse large B-cell lymphoma, with most genes exhibiting cell type specificity. Gene set analyses confirmed joint associations of unreported genes with breast and prostate cancer risk in UK Biobank data. Additional lung tissue single-cell RNA sequencing data with 113 individuals validated 18 of 32 (56.3%) statistically significant genes for lung cancer. Across cancers, 139 statistically significant genes were shared by at least 2 cancer types and were primarily enriched in specific immune cell types. CONCLUSION: Cell type-specific TWAS improve the identification of novel cancer susceptibility loci and provide insights into the immune landscape of cancer etiology.

Humans↗

PubMed2026

Precision-Based Filtering Facilitates Cross-Referencing of Conventional and Single-Nucleus Transcriptomes to Identify Time- and Temperature-Sensitive Cell Populations.

Transcriptome analysis via RNA sequencing (RNAseq) has become a ubiquitous method of molecular characterization from whole organisms, dissected tissues, and single cells. These experiments continue to provide an extraordinary volume of data describing molecular states and responses to many conditions. However, standard approaches to RNAseq analysis commonly use expression level filters that eliminate potentially useful data in the service of decreasing noise. Here we describe the implementation of a coefficient of variation-based filter for RNAseq gene expression data. This filter prioritizes consistent data across replicates, allowing lowly-expressed genes with low-variation measurements to be retained for downstream analysis. We show, using two independent Arabidopsis RNAseq datasets, that this filter allows for the inclusion of many more transcription factors than even a low-stringency expression level filter. This effect is independent of sequencing depth. We find that these lowly-expressed genes mark specific cell clusters in our single-nucleus (sn)RNAseq dataset and may facilitate future characterization of currently unknown cell types or states. We further characterize communities of co-expressed genes, sampled across the day at two growth temperatures, in relation to snRNAseq cell clusters, finding evidence for a highly photosynthetic cell population, and a cell state marked by high cell division and translation. These methods can be expanded to RNAseq analysis in many systems, facilitating the construction of more detailed models of tissue-specific gene regulatory networks.

Transcriptome analysis↗

PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗

PubMed2025

Inflammatory pathways and immune dysregulation in pediatric postoperative septic shock: A study integrating transcriptomics, machine learning and molecular docking.

This study elucidates the molecular and immune regulatory mechanisms of pediatric postoperative septic shock. Transcriptomic data were obtained from the Gene Expression Omnibus database. Differentially expressed genes were identified using the limma package, and gene co-expression modules were constructed using Weighted Gene Co-expression Network Analysis. Functional enrichment was performed via gene set enrichment analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses. Immune cell infiltration was assessed using ESTIMATE and CIBERSORT. Mendelian randomization was applied to explore causal relationships between gene expression and septic shock. Feature genes were selected using machine learning algorithms, and a diagnostic nomogram model was constructed. Finally, molecular docking analysis was performed to screen and evaluate the binding affinity of traditional Chinese medicine monomers to core target proteins. A total of 1331 differentially expressed genes were identified, and the turquoise module was strongly correlated with septic shock. Enrichment analysis revealed significant activation of IL-6/JAK/STAT3, TNF-α/NF-κB, and PI3K/Akt/mTOR pathways. Immune infiltration analysis indicated suppressed immune scores and imbalances in neutrophils, macrophages, T cells, and B cells. Mendelian randomization confirmed causal associations for 6 genes, including PIM3. The predictive model based on feature genes demonstrated high diagnostic performance. Molecular docking suggested that quercetin and astramembrannin I could stably bind PIM3. This study systematically identified core genes, dysregulated immune pathways, and candidate small-molecule interventions in pediatric septic shock, providing novel insights for early diagnosis and targeted therapy.

Humans↗

PubMed2024

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes↗

PubMed2026

Spatial Transcriptomics Identifies Characteristic Immunological Niches in Atopic Dermatitis.

BACKGROUND: Atopic dermatitis (AD) is primarily driven by a Type 2 immune response, with T helper (TH2) cells producing IL-4 and IL-13, thereby promoting inflammation, itch, and a compromised skin barrier. Yet, the spatial organization of pathogenic immune cells and their interactions with stromal and epithelial compartments in human AD skin remain incompletely understood. METHODS: We performed 10× Genomics Visium spatial transcriptomics on FFPE skin biopsies from patients with AD (n = 6), psoriasis (n = 2), and healthy controls (n = 5). Data were integrated with AD single-cell RNA sequencing (scRNA-seq) datasets and complemented by imaging mass cytometry (IMC) and multiplex immunofluorescence (IF) to validate the spatial localization of immune cells. Cell-cell communication analysis revealed putative signaling interactions within immune niches. RESULTS: Spatial clustering resolved tissue compartments and demonstrated transcriptional dysregulation in keratinocytes in AD and psoriasis. AD lesions showed a conserved spatial organization of immune aggregates within the superficial dermis. Integration of scRNA-seq signatures revealed spatially organized co-localization of T cells and mature migratory dendritic cells (mmDCs). We developed a ring-based neighborhood analysis to characterize the cellular organization of the immune-stromal niches, revealing T cell-enriched regions surrounded by inflammatory fibroblasts and activated keratinocytes. Intercellular communication analysis further identified putative signaling within mmDC-T cell niches that may promote pathogenic T cell recruitment and activation. Application of tertiary lymphoid structure (TLS) signatures indicated the presence of TLS-like regions. IMC and IF validated the close spatial proximity between activated TH2 cells and mmDCs. CONCLUSION: AD lesions contain spatially organized TLS-like immune niches at the dermal-epidermal junction, characterized by the close association of T cells and mmDCs and coordinated interactions with surrounding stromal and epithelial compartments. These mmDC-T cell niches may represent potential targets for future therapeutic strategies aimed at disrupting persistent local inflammatory pathways and improving long-term disease control.

atopic dermatitis↗

PubMed2026

Dysregulated Sheddase Signalling as a Molecular Driver of Plaque Instability Revealed by Integrative Transcriptomics.

Atherosclerosis is a major cause of mortality due to chronic and progressive low-grade inflammation and fibroproliferative remodelling of the intima of arteries. Comprehensive understanding of the interplay between plaque biology and the mechanisms underlying plaque vulnerability and rupture is essential. Here, we aimed to investigate the transcriptomic profiles of stable and unstable atherosclerotic plaques using RNA sequencing data from human carotid atherosclerotic plaque samples based on next-generation knowledge discovery (NGKD) methods. High-throughput RNA-seq data from plaques dissected in stable and unstable regions of four patients were obtained from the Gene Expression Omnibus (GEO) database. GEO RNA-seq Experiments Interactive Navigator (GREIN) software was used to obtain raw gene-level counts and filtered metadata for this dataset. The data were further filtered and normalized using Express analyst to derive differentially expressed genes (DEGs) in unstable plaques compared to stable plaques. The DEGs were further analysed using WebGestalt, STRING DB, preranked gene set enrichment analysis (GSEA), and Ingenuity Pathway Analysis (IPA) software. We identified 4792 DEGs in unstable plaques based on a p-value cutoff of < 0.05. NGKD analysis revealed that the sheddase pathway, collagen degradation, activation of matrix metalloproteinases (MMPs), and extracellular matrix (ECM) degradation ranked among the top five upregulated pathways, whereas the inhibition of MMPs and smooth muscle contraction pathways were identified as the most prominent downregulated pathways in unstable plaques. We found that the sheddase pathway was one of the most significantly upregulated canonical pathways in unstable plaques and this finding opens new avenues for potential therapeutic interventions in patients with atherosclerosis.

Humans↗

PubMed2026

Comparative Transcriptomic Analyses Identify Candidate Genes for Convergent Reproductive Shifts in a Bimodal Viviparous Amphibian.

Shifts in reproductive mode represent key evolutionary innovations that shape species' life histories and evolutionary trajectories. Species showing bimodal reproductive strategies with multiple independent origins offer a rare opportunity to gain insights into the adaptive processes and mechanisms underlying convergent traits. The fire salamander, Salamandra salamandra, is the only amphibian exhibiting intraspecific variation in reproductive mode across multiple independent reproductive shifts, enabling investigation of the transition between larviparity (females give birth to aquatic larvae) and pueriparity (females give birth to fully developed terrestrial juveniles) within a single species and across different timescales. Pueriparity is an adaptive innovation that skips the aquatic larval stage, allowing individuals to exploit habitats with no available water bodies. The fire salamander is larviparous across most of its range, but pueriparity has evolved independently at least three times: once in the early Pleistocene within S. s. bernardezi in the mountains of northern Spain, and more recently on two land-bridge islands (NW Spain) inhabited by S. s. gallaica. To identify candidate genes associated with these distinct reproductive modes, we compared gene expression profiles of the uterus and oviduct of pregnant females across two independent evolutionary transitions using RNA-sequencing. We detected shared changes in maternal gene expression among pueriparous S. s. bernardezi and S. s. gallaica relative to their larviparous counterparts, in addition to differences unique to each independent evolutionary transition. Functional enrichment analyses indicated that differentially expressed genes were associated with reproductive timing, angiogenesis, and maternal signalling, consistent with the phenotypic differences observed in the uterine environment and embryonic development between the two reproductive modes. This study represents an important first step towards understanding the genomic basis of the evolution of pueriparity in a remarkable bimodal reproductive system, and provides transcriptomic resources and candidate genes for future research into the genomic architecture underlying this poorly understood adaptive trait.

Animals↗

PubMed2026

Comparative Transcriptomic Analysis of Human Macrophages During Mycobacterium avium Versus Mycobacterium tuberculosis Infection.

The treatment of Mycobacterium avium (Mav) infection, responsible for over 80% of nontuberculous mycobacterial pulmonary disease, remains challenging due to rising antibiotic resistance and unsatisfactory success rates. Hence, there is a need for a deeper understanding of host-pathogen interactions to inform the development of alternative therapeutic approaches, like host-directed therapy (HDT), aimed at improving host antimycobacterial defenses. However, compared to Mycobacterium tuberculosis (Mtb) infections, knowledge of host-pathogen interactions for Mav infection is still limited. To address this knowledge gap, we performed a genome-wide host transcriptomic analysis of Mav-infected primary human macrophages-the primary host cell-alongside Mtb-infected macrophages to leverage insights from Mtb research. Our findings show substantial overlap in the gene expression patterns between Mav-infected and Mtb-infected macrophages, including induction of cytokine responses and modulation of various G-protein coupled receptors (GPCRs) involved in (lipid-mediated) macrophage immune functions. Notable differences were observed in the expression of immediate early genes (IEGs), phospholipases, and genes of the GTPase of immunity-associated protein (GIMAP) family. This study laid a foundation for identifying both shared and Mav-specific host response pathways, providing direction for future investigations into host-pathogen interactions during Mav infection and the identification of novel targets for HDT.

Humans↗

PubMed2025

Integrated genomic, transcriptomic, and metabolomic analyses of Chrysanthemum aromaticum provide insights into the volatile terpene biosynthesis.

Chrysanthemum aromaticum is renowned for its uniformly emitted strong and attractive scent, primarily attributed to volatile terpenes. Despite its commercial and horticultural significance, the molecular mechanisms underlying volatile terpene production in C. aromaticum remain largely unexplored. Here, we present the haplotype-resolved genome assembly of C. aromaticum, with a total size of 3.10 Gb, comprising nine anchored chromosomes with a contig N50 of 30.66 Mb and a scaffold N50 of 350.58 Mb. Phylogenetic analyses revealed a distant relationship between C. aromaticum and C. indicum, suggesting that C. aromaticum likely represents a distinct species rather than a variety of C. indicum. Through integrated genomic, transcriptomic, metabolomic, and biochemical analyses, we identified seven TPS involved in monoterpene biosynthesis and six TPS for sesquiterpene biosynthesis. Notably, comparative genomic analysis revealed a gene cluster for α-bisabolol biosynthesis in C. aromaticum, which has specifically expanded in Chrysanthemum species through tandem gene duplications, contributing to the elevated accumulation of α-bisabolol in the leaves of C. aromaticum. Our study provides important insights into the biosynthesis of volatile terpenes, highlighting the genetic basis for C. aromaticum's unique aromatic profile.

Chrysanthemum↗

PubMed2026

Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type, and sex specificity of gene expression with novel genetic risk for MERTK in female.

BackgroundAlzheimer's disease (AD), the most common age-related neurodegenerative disease, is closely associated with both amyloid-β plaque and neuroinflammation. Two thirds of AD patients are female, and they have a higher disease risk; women with AD have more extensive brain histological changes than men along with more severe cognitive symptoms and neurodegeneration.ObjectiveThis study aimed to determine how sex difference induces structural brain changes and molecular cell vulnerabilities in AD, with a focus on identifying sex-specific transcriptional alterations and genetic risk factors.MethodsWe performed single nucleus RNA sequencing on postmortem brains from individuals with AD and age- and sex-matched controls, focusing on the middle temporal gyrus, a cortical brain region strongly affected by the disease, and integrated single nucleus RNA sequencing results with genome-wide association study (GWAS) data using cell type-specific enrichment and generalized gene-set analysis approaches. The analysis pipeline is provided with threshold information.ResultsWe identified a selectively vulnerable subpopulation of layer 2/3 excitatory neurons that were RORB-negative and CDH9-expressing in both males and females. Disease-associated, but sex-independent, reactive astrocyte signatures were also present. In clear contrast, the microglia signatures of AD brains differed between males and females. Integrating single cell transcriptomic data with results from GWAS, we identified MERTK genetic variation as a candidate novel risk factor for AD selectively in females.ConclusionsTaken together, our single cell atlas of middle temporal gyrus revealed a unique cellular-level view of sex-specific transcriptional changes in AD, illuminating GWAS identification of sex-specific AD genes. These data serve as a rich resource for interrogation of the molecular and cellular basis of AD.

Alzheimer's disease↗

PubMed2026

scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.

BACKGROUND: Existing virtual perturbation methods can often infer directional changes by comparing predicted post-perturbation expression profiles with control cells. However, workflows that directly return direction-specific downstream candidate genes together with confidence scores, evidence support and interpretable summaries remain limited. We developed scGPA, an LLM-assisted workflow system for directional single-cell virtual gene perturbation analysis. METHODS: scGPA starts from raw single-cell RNA sequencing data and performs quality control, normalization, dimensionality reduction, clustering and cell-group selection. It then constructs cell-group-specific wild-type regulatory networks using repeated subsampling, principal component regression (PCR)/Ridge-based network inference and CP tensor denoising. Based on these networks, scGPA simulates dose-aware virtual knockdown of the target gene and applies signed perturbation propagation to estimate the magnitude and direction of downstream transcriptional responses. LLM assistance is used for marker-based cell-type annotation, evidence-guided candidate prioritization and user-facing biological summarization. RESULTS: We benchmarked scGPA across five public Perturb-seq datasets and compared its performance with GEARS, scGPT and a random baseline. The overall correct prediction rate of scGPA was 23.0%, exceeding those of GEARS (20.7%), scGPT (15.1%) and the random baseline (13.6%). These results indicate that scGPA achieved a higher correct prediction rate than the two comparator models and the random baseline. We subsequently evaluated scGPA using a public osteosarcoma single-cell dataset and performed qRT-PCR validation in 143B osteosarcoma cells. Among genes with significant experimental changes, scGPA achieved a directional concordance of 76.9%. When all tested downstream genes were counted, 37.0% were directionally correct, 51.9% showed no significant change and 11.1% changed in the opposite direction. CONCLUSIONS: scGPA provides a practical workflow system for predicting and prioritizing direction-specific downstream transcriptional responses after target-gene perturbation. By integrating single-cell regulatory network inference, signed virtual perturbation and LLM-assisted interpretation, scGPA supports target-gene function inference and downstream mechanistic investigation from single-cell transcriptomic data.

Single-Cell Gene Expression Analysis↗

PubMed2025

Transcriptome and metabolome profiling of the medicinal plant Dictamnus dasycarpus reveal key genes involved in quinoline alkaloids biosynthesis and limonoids biosynthesis.

BACKGROUND: As a member of Rutaceae family, Dictamnus dasycarpus Turcz. represents a prominent medicinal plant and economically valuable crop in traditional Chinese medicine, and is renowned for its therapeutic efficacy in treating dermatological conditions. The pharmacological activity of this species primarily stems from quinoline alkaloids and limonoids, which predominantly accumulate in the taproots. These bioactive compounds serve as critical determinants of both medicinal quality and crop yield. Nevertheless, the molecular mechanisms governing their dynamic accumulation patterns in D. dasycarpus taproots remain uncertain, and the fundamental biochemical basis underlying this process has yet to be elucidated. RESULTS: Metabolomic and transcriptomic analyses were carried out to investigate metabolites and gene expression during the development of D. dasycarpus taproots. The differentially accumulated secondary metabolites (DAMs) mainly included quinoline alkaloids and limonoids, and the accumulation of total alkaloids and total limonoids primarily occurred during 2- and 4-year-old. The differentially expressed genes (DEGs) are related to Glycolysis/Gluconeogenesis, Phenylalanine, tyrosine and tryptophan biosynthesis, Tryptophan metabolism, Terpenoid backbone biosynthesis, Sesquiterpenoid and triterpenoid biosynthesis, which had a close relationship with the accumulation of quinoline alkaloids and limonoids. Furthermore, we identified that some CYP450s, acetyltransferase, isomerase, 2-ODDs and others may play an important role in the process of producing quinoline alkaloids and limonoids. CONCLUSION: These results elucidated the molecular mechanisms and metabolic changes underlying the dynamic accumulation process occurring in the taproots of D. dasycarpus. These findings provide a theoretical basis for the planting and harvesting of D. dasycarpus.

Limonins↗

PubMed2026

Integrative analysis of transcriptome and chromatin accessibility reveals promoter-proximal regulation and identifies candidate ABC transporters associated with cold stress responses in maize.

BACKGROUND: Low-temperature stress is a formidable environmental constraint that severely limits the growth and productivity of maize (Zea mays L.), particularly during the highly vulnerable early seedling stage. While cold tolerance is a critical agronomic objective, the integrated transcriptional and epigenetic regulatory mechanisms that govern this trait remain largely elusive. Characterizing these coordinated molecular networks is fundamental to the genetic enhancement of cold resilience in maize. METHODS: Using two maize inbred lines contrasting in chilling response (ZHB12 tolerant, B73 sensitive), we performed integrative time‑course RNA‑seq and ATAC‑seq to thoroughly and systematically characterize the precise dynamic interplay between gene expression and chromatin accessibility under cold stress conditions at the seedling stage. RESULTS: Physiological assessments confirmed that ZHB12 possesses superior cold tolerance, manifested by significantly attenuated electrolyte leakage and reduced foliar damage compared to B73. Transcriptomic profiling revealed a massive, time-dependent divergence in gene expression between the two genotypes, with a major regulatory transition identified at 24 h of cold exposure. Functional enrichment analysis demonstrated that ZHB12 preferentially activates a robust defense repertoire, including Photosystem II electron transport, diterpenoid biosynthesis, and ATP biosynthetic pathways. Notably, multiple ATP-binding cassette (ABC) transporter genes were coordinately upregulated under chilling, suggesting their potential involvement in cellular homeostasis. ATAC-seq analysis indicated that cold stress is associated with chromatin remodeling in ZHB12, with increased accessibility observed in proximal promoter regions. Integrative analysis identified a core set of dual-responsive genes, in which increased promoter accessibility coincided with transcriptional upregulation. These genes were predominantly enriched in transporter activity and transcriptional regulation, suggesting potential epigenetic link to the superior stress response of ZHB12. CONCLUSION: Our findings reveal extensive transcriptional and chromatin accessibility changes in ZHB12 under cold stress. The observed associations between promoter accessibility and gene activation, particularly in genes involved in transport processes, highlight candidate regulators potentially contributing to cold tolerance. This study provides a molecular framework and identifies high-value candidate genes that may inform future efforts in breeding cold-tolerant maize, pending functional validation.

Zea mays↗

PubMed2026

Integrated methylome and transcriptome analysis provides insight into DNA methylation-mediated networks in sexual dimorphism of Vernicia montana.

BACKGROUND: Sexual dimorphism is fundamental to reproduction in dioecious plants and is regulated by both genetic and epigenetic mechanisms. DNA methylation is a central epigenetic mark known to influence phenotypic variation in plants. However, its specific role in shaping sexual dimorphism in dioecious trees remains poorly understood. To address this question, we performed integrated genome-wide DNA methylome and transcriptome analyses of four tissue types in the dioecious tung tree (Vernicia montana), including male and female flower buds and their corresponding leaves. RESULTS: Our analysis revealed distinct DNA methylation patterns between male and female tissues. Notably, the coordination between DNA methylation reprogramming and transcriptional regulation appeared to be more strongly associated with reproductive development than with vegetative growth in V. montana. We identified a set of sex-biased genes that may reflect different reproductive strategies between the sexes. Further analysis identified several key transcription factors (TFs) potentially associated with promoter differentially methylated regions (DMRs), including flowering-time regulators (e.g., FRS5, REM16, and VRN1) and TFs involved in hormone signaling pathways such as jasmonic acid, auxin, and salicylic acid signaling. Cis-regulatory element analysis showed that some promoter DMRs overlapped with hormone response elements related to abscisic acid, auxin, and gibberellin. Co-expression network analysis further revealed potential regulatory correlations among promoter DMR-mediated TFs, hormone-responsive pathways, and key floral development regulators. CONCLUSIONS: Collectively, our results suggest that interactions among DNA methylation, transcriptional regulation, and hormone-responsive pathways may contribute to the establishment of sexual dimorphism in V. montana. This study provides the first integrated view of these regulatory layers in V. montana and supports a species-specific regulatory framework for understanding the epigenetic basis of sexual dimorphism in this economically important dioecious tree. The proposed framework is based on multi-omics analyses and warrants further validation through targeted functional studies.

DNA Methylation↗

PubMed2026

Transcriptome-based high-frequency recurrence index predicts frequent recurrence in non-muscle-invasive bladder cancer after Bacillus Calmette-Guérin therapy.

BACKGROUND: High-frequency recurrence (HfR, ≥ 2 recurrences) in non-muscle-invasive bladder cancer (NMIBC) poses a significant clinical burden. Current risk models, such as the European Organization for Research and Treatment of Cancer (EORTC), the European Association of Urology (EAU), and the UROMOL classification, offer limited predictive accuracy for identifying patients at risk for frequent recurrence despite appropriate treatment. METHODS: A 75-gene high-frequency recurrence index (HfRI) was constructed by selecting recurrence-associated genes using differential expression and Cox regression analyses. The HfRI was computed as a weighted sum of normalized gene expression values. The model was trained on a discovery cohort and validated in multiple cohorts (n = 1379) using machine-learning approaches. Clinical relevance was assessed using recurrence-free survival (RFS) and Cox models, and predictive performance was compared with that of the EORTC, EAU, and UROMOL classifications using the area under the curve (AUC) and the concordance index (c-index). RESULTS: The HfRI robustly stratified patients into high-risk and low-risk groups across six independent NMIBC cohorts. Patients classified as HfRI-high had a significantly greater likelihood of experiencing ≥ 2 recurrences (χ2, p = 0.001) and showed markedly reduced RFS (log-rank test, p < 0.001). The adverse prognostic effect of the HfRI persisted even among patients treated with BCG therapy (log-rank test, p = 0.02). Multivariate analysis revealed that the HfRI was an independent predictor of HfR (HR = 2.82, 95% CI = 1.89-4.20, p < 0.001). Compared with established clinical risk classifiers, the HfRI demonstrated superior predictive performance (AUC = 0.736, c-index = 0.673) in terms of the EORTC (AUC = 0.594), EAU (AUC = 0.557) risk groups, and UROMOL2021 (AUC = 0.596) classification. Pathway analysis revealed that HfRI-high tumors were characterized by upregulation of cell cycle progression and DNA replication pathways, accompanied by suppression of immune signaling pathways. These biological features provide a mechanistic explanation for the reduced responsiveness to intravesical BCG therapy, underscoring the role of HfRI not only as a predictor of recurrence risk but also as a biomarker capable of identifying patients unlikely to benefit from standard BCG treatment. CONCLUSIONS: HfRI represents a robust, transcriptome-based tool for predicting frequent recurrence in NMIBC patients. The HfRI supports earlier identification of patients at risk of high-frequency recurrence, thereby supporting personalized treatment strategies.

Humans↗