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Identifying NDUFB2 as a prognostic biomarker for glioblastoma through an exploratory analysis of an anesthesia-related gene signature.
BACKGROUND: Glioblastoma (GBM) is the most aggressive primary brain tumor with poor prognosis despite multimodal therapy. Understanding the molecular and genetic characteristics of GBM and the influence of anesthesia drugs on tumor behavior is crucial for developing new therapeutic strategies. METHODS: We analyzed RNA-seq data from The Cancer Genome Atlas (TCGA)-GBM cohort and a Gene Expression Omnibus (GEO) dataset (GSE179004) of GBM samples treated with propofol and sevoflurane. Differential expression analysis identified anesthesia-related genes (ARGs), and their prognostic relevance was assessed using Cox regression. Consensus clustering stratified GBM patients into subgroups with distinct survival outcomes, immune cell infiltration, and pathway activities. An ARGs-based prognostic model was developed using Lasso-Cox regression and validated across cohorts. The hub gene NDUFB2 was identified and validated using single-cell sequencing, drug sensitivity assessment, and spatial transcriptome analysis. NDUFB2 expression levels were experimentally verified in our GBM samples. RESULTS: ARGs were significantly differentially expressed between GBM and normal tissues, with NDUFB2 identified as a hub gene associated with poor prognosis. Consensus clustering divided GBM patients into two subgroups with significant survival differences. An 11-ARGs-based prognostic model was established, demonstrating strong correlation with overall survival. NDUFB2 was predominantly expressed in malignant cells and associated with decreased survival and drug sensitivity. CONCLUSIONS: Our study, based on an initial exploratory analysis of anesthesia-related genes (ARGs), highlights their potential prognostic significance in GBM. We propose NDUFB2 as a robust biomarker for prognosis and therapeutic response, supported by extensive validation. These findings offer insights into the molecular classification of GBM and suggest a possible impact of anesthesia drugs on tumor progression, warranting further validation in larger cohorts.
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Single-Cell Omics in Legumes: Research Trends and Applications.
Legumes are important food crops and play a central role in sustainable agriculture through their ability to form symbiosis with rhizobia, soil bacteria that fix atmospheric nitrogen. Recent advances in single-cell and spatial transcriptomics, along with single-cell epigenomics, have enabled high-resolution analysis of gene expression dynamics and the prediction of cell-type-specific regulatory networks. In this review, we highlight recent progress in the use of single-cell omics in legumes, with a particular focus on how genes functioning in distinct cell types contribute to plant development, responses to pathogens, stress-induced plasticity, and the establishment of root nodule symbioses. Case studies in Medicago truncatula, Lotus japonicus, Glycine max, and Arachis hypogaea illustrate the shift from bulk to single-cell multi-omics. We conclude by outlining current limitations and future directions for building integrated legume cell atlases that will support translational research and crop improvement.
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Succinylation heterogeneity in lung adenocarcinoma: from prognostic model to KLK6-driven tumor microenvironment remodeling.
BACKGROUND: Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality. Protein succinylation, a key post-translational modification, is implicated in tumor progression. However, its comprehensive landscape and clinical significance in LUAD remain largely unexplored. METHODS: We integrated multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. A set of core succinylation-related genes was identified through differential expression and univariable Cox regression analyses. Molecular subtypes based on succinylation were determined by principal component analysis (PCA). A succinylation prognostic model was constructed via least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression. The differences of tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different risk groups were further explored. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics revealed effects of succinylation on TME. High-dimensional weighted gene co-expression networks analysis (hdWGCNA) was used to identify potential succinylation-related therapeutic targets. The function of therapeutic targets was further validated through scRNA-seq, spatial transcriptomics, and in vitro experiments. RESULTS: We identified 31 core succinylation-related genes and defined three molecular subtypes with distinct prognostic and TME characteristics. A robust 7-gene succinylation-based prognostic signature was developed and validated across 7 independent GEO cohorts, effectively stratifying patients into high- and low-risk groups with significant differences in survival, demonstrating high predictive accuracy, consistency, and clinical utility. The low-risk group exhibited an immunoreactive TME with enhanced immune cell infiltration and superior response to immunotherapy. scRNA-seq and spatial transcriptomics revealed enhanced succinylation in LUAD. Kallikrein-related peptidase 6 (KLK6) was identified as a potential therapeutic target. KLK6 was significantly upregulated in LUAD, correlated with poor prognosis and therapy resistance. KLK6 promoted global succinylation, proliferation, migration, and invasion of LUAD cells in vitro. Mechanistically, KLK6-positive tumor cells might foster an immunosuppressive TME by driving fibroblast-to-myofibroblast differentiation, enhancing extracellular matrix (ECM) deposition, and inhibiting CD8+ T cell infiltration. CONCLUSION: Our study delineates the succinylation landscape in LUAD, establishes a novel prognostic model for risk stratification and immunotherapy prediction. Meanwhile, we identified KLK6 as a potential promoter of tumor progression and immunosuppression. Targeting the succinylation pathway, particularly KLK6, may represent a promising therapeutic strategy for LUAD.
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Spatiotemporal single-cell analysis of gene expression in the mouse suprachiasmatic nucleus.
Mammalian circadian behaviors are orchestrated by the suprachiasmatic nucleus (SCN) in the ventral hypothalamus, but the number of SCN cell types and their functional roles remain unclear. We have used single-cell RNA-sequencing to identify the basic cell types in the mouse SCN and to characterize their circadian and light-induced gene expression patterns. We identified eight major cell types, with each type displaying a specific pattern of circadian gene expression. Five SCN neuronal subtypes, each with specific combinations of markers, differ in their spatial distribution, circadian rhythmicity and light responsiveness. Through a complete three-dimensional reconstruction of the mouse SCN at single-cell resolution, we obtained a standardized SCN atlas containing the spatial distribution of these subtypes and gene expression. Furthermore, we observed heterogeneous circadian gene expression between SCN neuron subtypes. Such a spatiotemporal pattern of gene regulation within the SCN may have an important function in the circadian pacemaker.
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In Situ Maturation and Tissue Adaptation of Type 2 Innate Lymphoid Cell Progenitors.
Innate lymphoid cells (ILCs) are generated early during ontogeny and persist predominantly as tissue-resident cells. Here, we examined how ILCs are maintained and renewed within tissues. We generated a single cell atlas of lung ILC2s and found that Il18r1+ ILCs comprise circulating and tissue-resident ILC progenitors (ILCP) and effector-cells with heterogeneous expression of the transcription factors Tcf7 and Zbtb16, and CD103. Our analyses revealed a continuous differentiation trajectory from Il18r1+ ST2- ILCPs to Il18r- ST2+ ILC2s, which was experimentally validated. Upon helminth infection, recruited and BM-derived cells generated the entire spectrum of ILC2s in parabiotic and shield chimeric mice, consistent with their potential role in the renewal of tissue ILC2s. Our findings identify local ILCPs and reveal ILCP in situ differentiation and tissue adaptation as a mechanism of ILC maintenance and phenotypic diversification. Local niches, rather than progenitor origin, or the developmental window during ontogeny, may dominantly imprint ILC phenotypes in adult tissues.
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Untimely TGFβ responses in COVID-19 limit antiviral functions of NK cells.
SARS-CoV-2 is a single-stranded RNA virus that causes COVID-19. Given its acute and often self-limiting course, it is likely that components of the innate immune system play a central part in controlling virus replication and determining clinical outcome. Natural killer (NK) cells are innate lymphocytes with notable activity against a broad range of viruses, including RNA viruses1,2. NK cell function may be altered during COVID-19 despite increased representation of NK cells with an activated and adaptive phenotype3,4. Here we show that a decline in viral load in COVID-19 correlates with NK cell status and that NK cells can control SARS-CoV-2 replication by recognizing infected target cells. In severe COVID-19, NK cells show defects in virus control, cytokine production and cell-mediated cytotoxicity despite high expression of cytotoxic effector molecules. Single-cell RNA sequencing of NK cells over the time course of the COVID-19 disease spectrum reveals a distinct gene expression signature. Transcriptional networks of interferon-driven NK cell activation are superimposed by a dominant transforming growth factor-β (TGFβ) response signature, with reduced expression of genes related to cell-cell adhesion, granule exocytosis and cell-mediated cytotoxicity. In severe COVID-19, serum levels of TGFβ peak during the first two weeks of infection, and serum obtained from these patients severely inhibits NK cell function in a TGFβ-dependent manner. Our data reveal that an untimely production of TGFβ is a hallmark of severe COVID-19 and may inhibit NK cell function and early control of the virus.
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Cross-System Transcriptomics Suggests Enterovirus A71 Exploits Wound Healing Programs to Target Neural Progenitors in Human Spinal Cord Organoids.
INTRODUCTION: The mechanisms of EV-A71 immune evasion and neuroinvasion are poorly understood. Inactivated vaccines provide protective immunity without the dysregulated inflammation and viral persistence caused by active infection. In this bioinformatics-driven study, our objective was to utilize computational transcriptomics to investigate this dichotomy and determine the specific neural cell populations susceptible to enteroviral infection. METHODS: We compared bulk RNA-seq data from THP-1 macrophages infected with active versus inactivated EV-A71 to identify viral hijacking programs. We then applied these systemic findings to the neural environment via an iterative Area Under the Curve (AUC)-based optimization strategy. This process refined the broad bulk-derived candidates into a 10-gene consensus signature, which was projected onto a human spinal cord organoid single-cell RNA-seq atlas to map viral susceptibility across distinct developmental lineages. RESULTS: While active infection with EV-A71 is associated with a wound-healing program defined by the expression of PMEPA1, ALOX5, CCL1, and SPRED3, the administration of an inactivated virus elicits an anti-inflammatory response without activating a similar transcriptional program. In fact, decreased expression of inflammatory factors (ADORA2A, CCR1, CXCL1) marks this wound-healing stage and leads to the development of an immunosuppressive M2 phenotype. By projecting the highly robust 10-gene consensus (comprising ALDH2, SERPINE2, and L1CAM) against the organoid atlas, we have predicted that the cycling neural progenitors could be the most likely reservoir for EV-A71 replication. Using a Wilcoxon rank sum test with Benjamini-Hochberg correction, we observed the only statistically enriched signature in progenitor cells Discussion: Transcriptomic data indicate that EV-A71 might hijack TGF-β/PMEPA1 and ALOX5 signaling pathways to establish EMT-like behavior. The local acquisition of an M2-like immunosuppressive state might help promote early systemic dissemination, immunologic escape, followed by colonization of the proliferative neural niche prior to the development of neurological symptoms. CONCLUSION: Computational identification of a macrophage gene signature provides robust discrimination of EV-A71 infection from vaccine-based protection in vivo. Mapping the signature to the context of human spinal cord organoids indicates that EV-A71 specifically targets proliferative and regenerative programs of neural stem cells.
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PEATmoss (Physcomitrella Expression Atlas Tool): a unified gene expression atlas for the model plant Physcomitrella patens.
Physcomitrella patens is a bryophyte model plant that is often used to study plant evolution and development. Its resources are of great importance for comparative genomics and evo-devo approaches. However, expression data from Physcomitrella patens were so far generated using different gene annotation versions and three different platforms: CombiMatrix and NimbleGen expression microarrays and RNA sequencing. The currently available P. patens expression data are distributed across three tools with different visualization methods to access the data. Here, we introduce an interactive expression atlas, Physcomitrella Expression Atlas Tool (PEATmoss), that unifies publicly available expression data for P. patens and provides multiple visualization methods to query the data in a single web-based tool. Moreover, PEATmoss includes 35 expression experiments not previously available in any other expression atlas. To facilitate gene expression queries across different gene annotation versions, and to access P. patens annotations and related resources, a lookup database and web tool linked to PEATmoss was implemented. PEATmoss can be accessed at https://peatmoss.online.uni-marburg.de.
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Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.
Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus-a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.
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Integrative Analysis of Glycosylation-Related Genes Reveals Prognostic Subtypes, Immune Evasion, and Therapeutic Vulnerabilities in Lung Adenocarcinoma.
Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and remains a leading cause of cancer-related mortality worldwide. Aberrant glycosylation contributes to tumor progression by regulating receptor signalling, immune evasion, and metastatic. However, the prognostic and therapeutic relevance of glycosylation-related genes (GRGs) in LUAD has not been comprehensively defined. Therefore, this study aimed to comprehensively evaluate GRG-associated molecular subtypes and their clinical and therapeutic relevance in LUAD. Methods: GRGs were curated from multiple public databases and integrated with transcriptomic and clinical data from The Cancer Genome Atlas LUAD cohort (TCGA-LUAD) with validation in Gene Expression Omnibus (GEO) datasets. Consensus clustering, pathway enrichment, and immune profiling were used to identify glycosylation-associated subtypes. A glycosylation activity scoring (Glyco. marker) was developed to quantify glycosylation features. Drug response prediction was analyzed using OncoPredict and the Genomics of Drug Sensitivity in Cancer (GDSC) database. Single-cell RNA sequencing (scRNA-seq) was analyzed to evaluate cell-type-specific GRG expression. Selected proteins were by immunohistochemistry (IHC) in LUAD tissue microarrays. Results: GRG expression stratified 513 LUAD patients into four molecular clusters with distinct clinical and immune characteristics. The Glyco.High group showed elevated expression of MGAT5 (mannosyl (α-1,6)-glycoprotein β-1,6-N-acetylglucosaminyltransferase), ST6GAL1 (β-galactoside α-2,6-sialyltransferase 1), GALNT7 (polypeptide N-acetylgalactosaminyltransferase 7), and FUT8 (fucosyltransferase 8), frequent tumor protein p53 (TP53) mutations, increased immune checkpoint expression, and enrichment of regulatory T cells. The Glyco. marker score predicted overall survival and was associated with stemness signatures. Drug response prediction suggested reduced sensitivity to platinum chemotherapy and epidermal growth factor receptor (EGFR) inhibitors but increased sensitivity to phosphoinositide 3-kinase/protein kinase B/mechanistic target of rapamycin (PI3K/AKT/mTOR) inhibitors. Conclusion: GRG-based molecular stratification identifies clinically distinct LUAD subtypes associated with immune regulation, tumor stemness, and therapeutic response. The Glyco. marker system provides a potential framework for prognostic assessment and precision oncology strategies in LUAD.