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PubMed2004

Omic space: coordinate-based integration and analysis of genomic phenomic interactions.

MOTIVATION: With the recent progress in genomics, various data sets of omic interactions describing networks of omic elements have become available. In order to obtain reliable hypotheses from the data, it is effective to integrate interactions from different sorts of data sets. In order to facilitate a coordinate-based integration and analysis of omic interactions, we introduce the concept of an omic space comprising a comprehensive set of omic planes. Genomic, transcriptomic, proteomic, metabolomic, phenomic and other omic planes are defined by two orthogonal genomic-coordinate axes. RESULTS: We show that the omic space concept helps us to assimilate biological findings comprehensively into hypotheses or models combining higher-order phenomena and lower-order mechanisms by demonstrating that a comprehensive ranking of correspondences among interactions in the space can be used effectively for estimating candidates of responsible gene pairs for epistatic interacting loci of tumors in mice. We also show that the omic space offers a convenient framework for database integration, by presenting a system named the 'Genome <==> Phenome Superhighway' (GPS) that serves as a framework for integration and visualization of omic interactions based on omic spaces of some model species including Homo sapiens, Mus musculus, Caenorhabditis elegans and Arabidopsis thaliana. AVAILABILITY: For the GPS web site, see http://omicspace.riken.jp/gps/.

Algorithms↗

PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

MOTIVATION: Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. RESULTS: We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g. associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell composition. Our MPAC R package enables similar multi-omic analyses on new datasets. AVAILABILITY AND IMPLEMENTATION: The MPAC package is available at Bioconductor https://bioconductor.org/packages/MPAC.

Humans↗

PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g., associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Journal Article↗

PubMed2026

Machine learning-integrated multi-omics risk prediction for pulmonary fungal infection in COPD and lung cancer: a transcriptomic and immune profiling study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and lung cancer are major risk factors for invasive pulmonary fungal infection (IPFI), carrying an attributable mortality of 30%-80%. Their coexistence further amplifies immunosuppression, while current diagnostic criteria remain inadequate for early risk identification. METHODS: Transcriptomic data from the GEO dataset GSE296912 (scRNA-seq; 12,078 cells from normal and COPD lung tissue) and The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) bulk RNA-seq cohort (539 tumor and 59 normal samples) underwent differential expression and cross-omics integration analysis. Five machine learning models were constructed: logistic regression, SVM, random forest, XGBoost, and LASSO. Candidate genes were validated by qRT-PCR in A549 cells and THP-1-derived macrophages stimulated with heat-inactivated Aspergillus fumigatus conidia, a protocol selected to ensure BSL-2 biosafety compliance and isolate PAMP-mediated innate immune signaling. Model performance was evaluated using 5-fold stratified cross-validation with AUC, calibration curves, and decision curve analysis. RESULTS: Single-cell transcriptomic analysis of 12,078 cells identified 14 distinct cell populations, with marked myeloid expansion and immune dysregulation in COPD lung tissue. Cross-omics integration with TCGA-LUAD data identified 1,145 shared genes (79 immune-related), converging on NF-κB, TLR4, and cytokine receptor signaling. The random forest model achieved excellent discriminative performance (5-fold CV AUC = 0.988), with Treg infiltration, TLR4, and MMP9 as the top predictors. qRT-PCR confirmed significant upregulation of all five candidate genes (DEFB4A, S100A8, IL-8, MMP9, and TLR4) in both A549 and THP-1 cells following fungal stimulation. CONCLUSION: This multi-omics machine learning model integrating scRNA-seq and TCGA transcriptomic data demonstrates excellent discriminative performance (AUC = 0.988), with mechanistic convergence of NF-κB, TLR4, and oncogenic signaling pathways identified across shared immune gene signatures. In vitro qRT-PCR validation confirms the biological relevance of five key antifungal immune genes, providing a transcriptomic foundation for future prospective IPFI risk stratification in patients with COPD and lung cancer.

TLR4↗

PubMed2026

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acquired resistance remains a critical bottleneck, severely compromising their long-term clinical efficacy. The pan-cancer molecular landscape and heterogeneous mechanisms driving this resistance, ranging from genetic alterations to dynamic network rewiring, remain poorly understood. METHODS: We integrated large-scale pharmacogenomic profiling of the FGFR inhibitor AZD4547 from the GDSC2 and PRISM databases with single-cell RNA sequencing to dissect the multi-omics landscape of FGFRi resistance across 312 cell lines from 8 cancer types. This multi-omics framework was further extended by machine learning modeling and systematic synthetic lethality screening to uncover actionable therapeutic targets. In vitro viability assays and western blot analysis were subsequently conducted to experimentally evaluate the predicted FGFR-EGFR synthetic lethality. RESULTS: Our dual-database analysis unveiled a multi-dimensional atlas of FGFRi resistance. We identified cancer-specific genomic drivers, such as ELF4 amplification in glioblastoma, alongside key transcriptomic markers including UCP2 and FSCN1, highlighting a shift towards metabolic reprogramming and epithelial-mesenchymal transition (EMT). Single-cell analysis unveiled that resistance is linked to the heterogeneous enrichment of baseline subpopulations characterized by distinct metaprograms, including cell-cycle dysregulation. Furthermore, a random forest model built on a LASSO-derived transcriptomic signature was constructed, demonstrating promising predictive capability for AZD4547 sensitivity (mean test-set AUC = 0.73, 95% CI [0.63, 0.80]); the signature generalized well to erdafitinib but showed limited transferability to some other FGFR inhibitors (e.g. pemigatinib, BGJ398). Most notably, our synthetic lethal screening revealed a convergent reliance on compensatory RTK signaling (specifically EGFR pathway enrichment) and downstream MAPK/PI3K cascades in resistant phenotypes, providing converging computational evidence for EGFR pathway activation as an adaptive bypass mechanism. This predicted synthetic lethality was experimentally supported in two FGFR-dependent cell line models (RT112 and CCLP1), in which combined FGFR-EGFR inhibition produced marked synergistic antiproliferative effects. CONCLUSIONS: This study establishes a comprehensive multi-omics atlas of resistance to the FGFR inhibitor AZD4547, delineating convergent mechanisms of metabolic reprogramming and EGFR-mediated bypass signaling. Our findings characterize the resistance as a dynamic network rewiring and nominate rational combination strategies to overcome this therapeutic bottleneck. While FGFR-EGFR co-inhibition is experimentally supported, metabolic co-targeting remains a computationally derived, hypothesis-generating strategy.

Benzamides↗

PubMed2025

Using Callus as an Ex Vivo System for Chromatin Analysis.

Next-generation sequencing has revolutionized epigenetics research, enabling a comprehensive analysis of DNA methylation and histone modification profiles to explore complex biological systems at unprecedented depth. Deciphering the intricate epigenetic mechanisms that regulate gene activity presents significant challenges, including the issue of analyzing heterogeneous cell populations in bulk. Bulk analysis introduces bias and can obscure crucial information by averaging readouts from distinct cells. Various approaches have been developed to address this issue, such as cell-type-specific enrichment or single-cell sequencing techniques. However, the need for transgenic lines with fluorescent markers, along with technical challenges such as efficient protoplast isolation and low yield, limits their widespread adoption and use in multi-omic studies. This review discusses the pros and cons of these approaches, providing a valuable basis for selecting the most suitable strategy to minimize heterogeneity. We will also highlight the use of cotyledon-derived callus as an ex vivo system as a simple, accessible, and robust platform for enabling high-throughput multi-omic analyses.

Chromatin↗

PubMed2026

Genome-wide identification and functional analysis of the BES1-like (VfBES1) gene family in Vernicia fordii reveals its role in floral development.

BACKGROUND: Vernicia fordii Hemsl (also known as Tung tree), an significant commercial oil-producing tree species, is a monoecious and diclinous species with male and female flowers on the same inflorescence; however, the molecular mechanisms governing its floral sex determination remain elusive, particularly the genetic basis underlying the skewed female-to-male flower ratio and the evolutionary dynamics of sex-related gene families, which severely restrict targeted breeding for yield enhancement. In the model plant Arabidopsis, the BRI1 EMS SUPPRESSOR 1 (BES1) transcription factor family plays a crucial role in Brassinosteroid (BR) signaling and reproductive development. However, its function remains largely unexplored in woody perennials. RESULTS: In this study, we introduce the genome-wide identification and functional characterization of the BES1-like (VfBES1) gene family in the Tung tree for the first time. Integrative multi-omics approaches reveal seven VfBES1 genes that are clustered into three phylogenetically distinct clades, each characterized by clade-specific motifs and structural simplicity. Segmental duplication events (VfBES1-1/VfBES1-5 and VfBES1-4/VfBES1-7) and promoter cis-element enrichment (hormone-responsive and abiotic stress-related motifs) highlight evolutionary innovation and functional diversification. Spatiotemporal expression profiling reveals VfBES1 genes' tissue- and stage-specific roles. VfBES1-1 predominantly expresses in female flowers and fruits, suggesting its possible roles in late-stage sex maintenance or ovule and fruit development. VfBES1-2 and VfBES1-6 exhibit male flower-specific and early floral developmental activation, respectively. Nuclear-localized VfBES1-6 displays co-expression with VfMYB35-1 gene, which is a regulator of male structure degeneration. CONCLUSIONS: Findings in this study shed light on the regulatory roles of VfBES1 genes in the floral development of the Tung tree, providing a reference for its precision breeding to enhance flowering synchrony and seed productivity. This study also provides a comparative framework for understanding the functional diversity of BES1-like genes in non-model woody plants.

Flowers↗

PubMed2026

Multi-omics reveals that burdock seed aglycone alleviates renal fibrosis by restoring mitochondrial oxidative phosphorylation function.

Renal fibrosis (RF), a common pathological process driving chronic kidney disease (CKD) progression to end-stage renal failure, is closely associated with oxidative phosphorylation (OXPHOS). Arctigenin (ATG), the main active component of burdock seed, exhibits anti-inflammatory and anti-fibrotic activities, but its mechanisms in RF treatment remain unclear. Here, we performed integrated transcriptomic and proteomic analyses to identify key targets and pathways of ATG in a unilateral ureteral obstruction-induced rat RF model. Multi-omics enrichment analysis revealed that NDUFS8 and NDUFS2 were the core targets of ATG, with the OXPHOS pathway as the central intersecting pathway. Our results suggest that ATG exerts anti-renal fibrosis effects by targeting the OXPHOS pathway to inhibit excessive reactive oxygen species production and oxidative stress. SIGNIFICANCE: Chronic kidney disease (CKD) continues to impose an escalating global health and socioeconomic burden, while renal fibrosis (RF), as the convergent pathological endpoint of virtually all progressive nephropathies, remains the principal determinant of irreversible renal failure and adverse clinical outcomes. Despite extensive efforts to develop antifibrotic therapies, effective clinical interventions remain elusive, largely due to the complex and multifactorial nature of RF pathogenesis. In this study, we employed an integrated multi-omics framework encompassing transcriptomics, proteomics, and metabolomics to systematically decipher the antifibrotic mechanism of arctigenin (ATG), a bioactive natural compound derived from traditional Chinese medicine. Our findings identify mitochondrial oxidative phosphorylation as the pivotal regulatory axis underlying the renoprotective effects of ATG and further establish key catalytic subunits of mitochondrial complex I as its direct molecular targets. Mechanistically, ATG not only restores complex I activity and reprograms mitochondrial energy metabolism but also preserves the intracellular stability and localization of these subunits, thereby preventing their aberrant release-mediated inflammatory activation and disrupting the self-perpetuating cycle linking metabolic dysfunction, inflammation, and fibrosis progression. Beyond revealing a previously unrecognized dual mechanism integrating metabolic and inflammatory regulation, this study provides compelling evidence that mitochondrial dysfunction is not merely a secondary consequence of tissue injury but a fundamental driver of fibrotic remodeling. Importantly, our work highlights the translational potential of natural product-based mitochondrial interventions for CKD treatment and supports a broader conceptual shift toward metabolism-centered therapeutic strategies for chronic fibrotic diseases. Given the central role of mitochondrial dysfunction across multiple organs, these findings may also have far-reaching implications for the treatment of systemic fibrosis-related disorders beyond the kidney.

Animals↗

PubMed2025

Cardiovascular Complications Are Increased in Inflammatory Bowel Disease: A Path Toward Achievement of a Personalized Risk Estimation.

Background/Objectives: The global burden of inflammatory bowel diseases (IBDs) continues to rise, with up to 50% of patients experiencing extraintestinal manifestations. Cardiovascular diseases (CVDs) are of particular concern, ranking as the second leading cause of mortality in this population. Despite a comparatively lower prevalence of traditional cardiovascular (CV) risk factors, the persistent inflammatory milieu and immune dysregulation inherent to IBD may contribute to heightened CVD risk. In this study, following a review of the current literature, an ongoing prospective trial designed to clarify CV risk profiles in IBD patients is detailed. Methods: A cohort of patients with IBD is being enrolled for comprehensive baseline evaluation of CV risk factors, lifestyle metrics, and disease characteristics. The incidence of major adverse cardiovascular events (MACEs) will be tracked and contrasted with a gender- and age-matched non-IBD cohort over a 2-year follow-up period. In cases of MACE occurrence, a multi-omics analysis-including genomic, proteomic, transcriptomic, and microbiome profiling-will be performed, along with a parallel evaluation in matched IBD controls without MACE. An artificial intelligence (AI) framework will support the analysis of this complex dataset. Results: To date, over 150 patients with IBD have been enrolled, and detailed phenotypic data and biological samples have been collected. Conclusions: We aim to introduce an IBD-specific correction factor for existing CV risk scores upon study completion. This is particularly relevant for individuals under 40 years of age, who are often inadequately assessed by current risk stratification models.

Crohn’s disease↗

PubMed2025

Potential biomarkers for human Ascending aortic aneurysm identified through metagenomic and metabolomic analyses: A case-control study.

INTRODUCTION: Ascending aortic aneurysm (AsAA) is a high-risk cardiovascular condition; recent research indicates a possible association between gut microbiota, plasma metabolites, and the pathogenesis of AsAA. OBJECTIVE: This study aims to investigate the compositional and metabolic alterations in the gut microbiota of AsAA patients to identify potential biomarkers for AsAA. METHODS: This study enlisted 72 participants, comprising 44 individuals with AsAA and 28 healthy controls. All participants underwent examination for clinical features, and fecal and plasma samples were obtained for metagenomic and metabolomic studies. RESULTS: Metagenomic analysis revealed a significant reduction of 23 bacterial species in AsAA patients, including Bifidobacterium adolescentis, Bifidobacterium longum, Lactiplantibacillus plantarum, Enterococcus faecalis, and Streptococcus thermophilus, while 52 bacterial species, such as Prevotella copri, Phascolarctobacterium faecium, and Eubacterium ventriosum, were found to be enriched. Furthermore, we identified seven microbial co-abundance groups (CAGs), of which three (predominantly comprising Roseburia, Agathobacter, and Prevotella) were significantly elevated in AsAA patients, whereas one (predominantly comprising Escherichia) was substantially diminished. KEGG pathway enrichment analysis indicated that the biosynthesis of unsaturated fatty acids pathway displayed the most pronounced differences between groups. Metabolomics data revealed that 22 metabolites, including ceramides, were significantly elevated, while 8 metabolites, such as threonine, were notably downregulated. Moreover, clinical indicators like C-reactive protein (CRP) and complement components C3 and C4 have shown strong correlations with specific gut microbiota (Streptococcus, Prevotella) and plasma metabolites (threonine, ceramides). These findings indicate that inflammatory responses, metabolic dysregulation, and gut microbiota imbalance are pivotal in the etiology of AsAA. CONCLUSION: This study demonstrates substantial alterations in gut microbiota composition and plasma metabolites in patients with AsAA. Prevotella and ceramides exhibit potential as biomarkers for AsAA diagnosis. Furthermore, a synergy of Prevotella and ceramides may function as a potent disease prediction classifier, offering novel perspectives on the early diagnosis and targeted treatment of AsAA.

Humans↗

PubMed2024

Post-transcriptional control drives Aurora kinase A expression in human cancers.

Aurora kinase A (AURKA) is a major regulator of the cell cycle. A prominent association exists between high expression of AURKA and cancer, and impairment of AURKA levels can trigger its oncogenic activity. In order to explore the contribution of post-transcriptional regulation to AURKA expression in different cancers, we carried out a meta-analysis of -omics data of 18 cancer types from The Cancer Genome Atlas (TCGA). Our study confirmed a general trend for increased AURKA mRNA in cancer compared to normal tissues and revealed that AURKA expression is highly dependent on post-transcriptional control in several cancers. Correlation and clustering analyses of AURKA mRNA and protein expression, and expression of AURKA-targeting hsa-let-7a miRNA, unveiled that hsa-let-7a is likely involved to varying extents in controlling AURKA expression in cancers. We then measured differences in the short/long ratio (SLR) of the two alternative cleavage and polyadenylation (APA) isoforms of AURKA mRNA across cancers compared to the respective healthy counterparts. We suggest that the interplay between APA and hsa-let-7a targeting of AURKA mRNA may influence AURKA expression in some cancers. hsa-let-7a and APA may also independently contribute to altered AURKA levels. Therefore, we argue that AURKA mRNA and protein expression are often discordant in cancer as a result of dynamic post-transcriptional regulation.

Humans↗

PubMed2026

Multi-omics reveal microbial functional traits and antifungal metabolites associated with lower Pseudogymnoascus destructans loads in bat cave soils.

White-nose syndrome, caused by Pseudogymnoascus destructans (Pd), is a major fungal disease threatening hibernating bats. Cave soils can serve as environmental reservoirs for Pd, yet the microbial and biochemical mechanisms underlying naturally low Pd burdens in some cave environments remain poorly understood. Here, we integrated soil microbiome profiling, metagenomics, metabolomics, multi-omics network analysis, and in vitro validation to investigate the ecological and functional basis of differential Pd loads in hibernating bat caves in Northeast China. The three caves shared cold, humid, and weakly acidic microenvironments, but differed significantly in electrical conductivity, soil water content, nutrient availability, and extracellular enzyme activities. Soil microbial communities showed significant inter-cave variation in composition, diversity, and niche breadth, with stochastic processes contributing substantially to community assembly. Environmental variables, particularly pH and Pd load, were important predictors of microbial community structure. Functional analyses revealed that the low-Pd Gezi Cave was enriched in genes associated with organic carbon degradation, nitrogen input and retention, and secondary metabolism. Metabolomic profiling further identified cave-specific metabolite signatures, among which Biochanin A, 4-Hydroxybenzaldehyde, Vanillin, and Arachidonic acid were negatively correlated with Pd loads. Integrated pathway and network analyses showed that differential genes and metabolites jointly mapped to secondary metabolite biosynthesis, aminobenzoate degradation, and flavonoid degradation pathways, forming a microbe-metabolite-functional gene coupling network involving key taxa such as Rhodococcus, Pseudorhodoplanes, and Rhodoplanes. In vitro assays confirmed that 4-Hydroxybenzaldehyde, Coumarin, and Vanillin inhibited Pd growth. Structural equation modelling further indicated that environmental heterogeneity was associated with variation in Pd loads through microbial functional attributes and metabolite profiles. These findings suggest that naturally low-Pd cave soils are associated with coordinated environmental filtering, microbial functional specialization, and antifungal metabolite production, providing mechanistic insight into microbial and biochemical constraints on Pd persistence in cave reservoirs.

Animals↗

PubMed2026

Plasma proteins are integral to cross-tissue gene regulatory networks implicated in cardiometabolic disorders and coronary artery disease.

The plasma proteome has demonstrated promise for identifying diagnostic markers for cardiometabolic disorders (CMDs) and coronary artery disease (CAD). However, identifying the organ of origin for these biomarkers is critical for establishing biological relevance. We performed a multi-omic integrative analysis across multiple tissues from the STARNET study by profiling 974 plasma proteins in 532 CAD patients, integrating RNA sequencing (RNA-seq) data from the arterial wall, major metabolic organs, and blood. We identified 144 cis-protein quantitative trait loci in plasma, colocalizing with tissue cis-expression quantitative trait loci. Additionally, by mapping tissue mRNA "seed genes," we traced 262 plasma proteins to their source organs, primarily the liver. Crucially, we found that 851 plasma proteins are associated with the activity of cross-tissue gene regulatory networks (GRNs), including GRNs implicated in CMD and CAD development. Our findings demonstrate that plasma proteins are integral components of GRNs, with potential for developing reliable diagnostics and precise therapeutic targets. A record of this paper's transparent peer review process is included in the supplemental information.

cardiometabolic disorders↗

PubMed2002

Carbohydrate analysis prepares to enter the "Omics" era.

In this issue, Houseman and Mrksich describe a carbohydrate array preparation method that can be used to analyze protein-carbohydrate interactions and to characterize the substrate specificity of a carbohydrate-modifying enzyme. Carbohydrate chips were prepared by a novel procedure that allows the covalent attachment of carbohydrate-diene conjugates to a specially engineered monolayer surface. The surface presents a precisely controllable ratio of reactive benzoquinone and inert ethylene glycol groups. Nonspecific adsorption of proteins to the surface is extremely low, and the surface is compatible with popular detection techniques. The immobilization technique was demonstrated to be compatible with recently developed automated solid phase carbohydrate synthesis methods, paving the way for the development of highly complex carbohydrate arrays.

Adsorption↗

PubMed2025

Alternative spliceosomal protein Eftud2 mediated Kif3a exon skipping promotes SHH-subgroup medulloblastoma progression.

Alternative splicing plays a pivotal role in various facets of organogenesis, immune response, and tumorigenesis. Medulloblastoma represents a prevalent childhood brain tumor, with approximately one-third classified as the Sonic Hedgehog (SHH) subgroup. Nevertheless, the contribution of alternative splicing to medulloblastoma oncogenesis remains elusive. This investigation delineated an upregulation of the spliceosomal protein Eftud2 in the SHH-subgroup medulloblastoma mouse model and human medulloblastoma patients. Targeted ablation of Eftud2 in granule precursor cells (GNPs) within the cerebellum prolonged the survival of SHH-subgroup medulloblastoma mice, indicating a putative association between Eftud2 expression and medulloblastoma prognosis. Functional assays unveiled that EFTUD2 depletion in human medulloblastoma cells significantly curtailed cellular proliferation by impeding the activation of the SHH signaling pathway. Through multi-omics sequencing analysis, it was discerned that Eftud2 influences exons 10-11 skipping of Kif3a, a kinesin motor critical for primary cilia formation. Notably, exons 10-11 skipping in Kif3a augmented human medulloblastoma cell proliferation by potentiating the transcriptional activity of Gli2. These findings underscore a robust correlation between Eftud2 and SHH-subgroup medulloblastoma, emphasizing its regulatory role in modulating downstream transcription factors through the alternative splicing of pivotal genes within the SHH signaling pathway, thereby propelling the aggressive proliferation of SHH-subgroup medulloblastoma.

Medulloblastoma↗

PubMed2026

Mutational signatures in blood-brain barrier: mechanisms, computational insights, and clinical applications in precision oncology.

The blood - brain barrier (BBB) plays a central role in maintaining central nervous system (CNS) homeostasis, and its disruption is a defining feature of malignant brain tumors such as glioblastoma. Emerging evidence indicates that BBB dysfunction not only alters the tumor microenvironment but also shapes the mutational processes that drive genomic instability in CNS malignancies. This review synthesizes current understanding of the biological mechanisms linking BBB breakdown with distinct mutational signatures, including those arising from oxidative stress, hypoxia-induced replication stress, lipid peroxidation, inflammation, and metabolic reprogramming. Advances in next-generation sequencing, coupled with computational tools such as non-negative matrix factorization, Bayesian modeling, and deep learning, have enabled precise extraction of these signatures and their integration with multi-omics data. Clinically, BBB-associated mutational signatures offer significant promise for therapeutic stratification, prediction of treatment response, and noninvasive monitoring through cerebrospinal fluid - derived circulating tumor DNA. Despite these advances, challenges persist due to limited tissue accessibility, low-yield CSF samples, incomplete mechanistic models, and the lack of CNS-specific analytical frameworks. A deeper understanding of BBB-driven mutational processes, supported by improved computational approaches and integrative datasets, holds potential to advance precision oncology in neuro-oncology.

Humans↗

PubMed2026

IL-33 Drives Inflammatory Changes and Extracellular Trap Formation in Eosinophils Involving Oxidised LDL and Complement Pathways.

BACKGROUND: IL-33 levels are elevated in the airways of patients with eosinophilic diseases, and IL-33 receptor expression on eosinophils is upregulated in type 2-high environments. However, the role of IL-33 in the regulation of human eosinophils remains unclear. OBJECTIVE: To elucidate the inflammatory effects of IL-33 on the cellular function of human eosinophils. METHODS: Blood eosinophils were stimulated with IL-33, TNF-α, oxidised low-density lipoprotein (oxLDL) and complement fragments (C3a and C5a). Multi-omics analyses, including transcriptomics and proteomics, were performed. Extracellular trap formation (ETosis) was assessed by SYTOX nucleic acid staining and was visualised by immunofluorescence and transmission electron microscopy. RESULTS: Multi-omics analyses revealed an IL-33- and TNF-α-induced inflammatory gene signature characterised by the upregulation of cell surface markers (oxLDL receptor 1, CD22, CD4 and ICAM-1) and inflammatory mediators (C3, CCL3/4 and IL1A/B). CD22 upregulation was specific to IL-33 stimulation. Eosinophils derived from nasal polyps exhibited a gene expression profile similar to that of IL-33-stimulated eosinophils. Functional assays demonstrated that oxLDL and complement fragments differentially prolonged eosinophil survival and altered the expression of adhesion molecules. OxLDL- and complement fragment-induced gene signatures were partly detected in eosinophils derived from nasal polyps. Furthermore, IL-33 triggered ETosis via NADPH oxidase, mitogen-activated protein kinase and phosphoinositide 3-kinase pathways. CONCLUSIONS: IL-33, in conjunction with oxLDL and the complement cascade, induces inflammatory changes in eosinophils, promoting an ETosis-prone phenotype. These pathways represent potential therapeutic targets in refractory eosinophilic diseases.

Humans↗

PubMed2026

Adaptive genomic evolution and WD40-regulated temporal dynamics of anthocyanins support leaf photoplasticity in Parrotia subaequalis.

BACKGROUND: Parrotia subaequalis, a Tertiary relict endemic to China, plays a significant role in phylogeny and adaptive evolution as a key species in the early differentiation of angiosperms. It has abundant leaf colors and great potential as an ornamental tree. RESULTS: This study assembled the first chromosome-level genome of P. subaequalis (Contig N50 = 2.15 Mb), revealing transposable element proliferation, key paleopolyploid events and dynamic gene family evolution, including the expansion of secondary metabolite transport and synthesis genes (such as WD40, 2OG-FeII_Oxy) and the contraction of gene families related to flower morphogenesis (such as F-box-like, K-box). Through integrative transcriptomics and targeted metabolomics approaches, we further revealed that the color transition of young leaves from red to green was driven by temporal accumulation differences of malvidin-3,5-O-diglucoside, whose biosynthesis is progressively down-regulated during leaf development. WGCNA revealed that a subset of WD40 genes (light-signaling, TTG1/HOS15-like, etc.) coexpresses with anthocyanin biosynthetic genes, like 4CLL9, GT1, in anthocyanin-related modules enriched for auxin signaling and hydrolase activity, suggesting a potential link between WD40 expansion and photoprotective plasticity. Relevant regulatory networks were found to complement the species-specific gene pool related to leaf color regulation. CONCLUSION: This genomic resource of P. subaequalis advanced our understanding of early angiosperm adaptation through neofunctionalized regulatory networks and established a foundation for molecular breeding aimed at enhancing environmental resilience while preserving ornamental traits.

Anthocyanins↗