Search Results (835)

Pediatric asthma is a multifactorial and heterogeneous disease determined by the dynamic interplay of genetic susceptibility, environmental exposures, and immune dysregulation. Recent advances have highlighted the pivotal role of epigenetic mechanisms, in particular, DNA methylation, histone modifications, and non-coding RNAs, in the regulation of inflammatory pathways contributing to asthma phenotypes and endotypes. This review examines the role of respiratory viruses such as respiratory syncytial virus (RSV), rhinovirus (RV), and other bacterial and fungal infections that are mediators of infection-induced epithelial inflammation that drive epithelial homeostatic imbalance and induce persistent epigenetic alterations. These alterations lead to immune dysregulation, remodeling of the airways, and resistance to corticosteroids. A focused analysis of T2-high and T2-low asthma endotypes highlights unique epigenetic landscapes directing cytokines and cellular recruitment and thereby supports phenotype-specific aspects of disease pathogenesis. Additionally, this review also considers the role of miRNAs in the control of post-transcriptional networks that are pivotal in asthma exacerbation and the severity of the disease. We discuss novel and emerging epigenetic therapies, such as DNA methyltransferase inhibitors, histone deacetylase inhibitors, miRNA-based treatments, and immunomodulatory probiotics, that are in preclinical or early clinical development and may support precision medicine in asthma. Collectively, the current findings highlight the translational relevance of including pathogen-related biomarkers and epigenomic data for stratifying pediatric asthma patients and for the personalization of therapeutic regimens. Epigenetic dysregulation has emerged as a novel and potentially transformative approach for mitigating chronic inflammation and long-term morbidity in children with asthma. Full article

Glioblastoma (GBM) is a highly aggressive and heterogeneous brain tumor. Glioma stem-like cells (GSCs) play a central role in tumor progression, therapeutic resistance, and recurrence. Although immune cells are known to shape the GBM microenvironment, the impact of antigen-presenting-cell (APC) signature genes on tumor-intrinsic phenotypes remains underexplored. We analyzed both bulk- and single-cell RNA sequencing datasets of GBM to investigate the association between APC gene expression and tumor-cell states, including stemness and metabolic reprogramming. Signature scores were computed using curated gene sets related to APC activity, KEGG metabolic pathways, and cancer hallmark pathways. Protein–protein interaction (PPI) networks were constructed to examine the links between immune regulators and metabolic programs. The high expression of APC-related genes, such as HLA-DRA, CD74, CD80, CD86, and CIITA, was associated with lower stemness signatures and enhanced inflammatory signaling. These APC-high states (mean difference = –0.43, adjusted p < 0.001) also showed a shift in metabolic activity, with decreased oxidative phosphorylation and increased lipid and steroid metabolism. This pattern suggests coordinated changes in immune activity and metabolic status. Furthermore, TNF-α and other inflammatory markers were more highly expressed in the less stem-like tumor cells, indicating a possible role of inflammation in promoting differentiation. Our findings revealed that elevated APC gene signatures are associated with more differentiated and metabolically specialized GBM cell states. These transcriptional features may also reflect greater immunogenicity and inflammation sensitivity. The APC metabolic signature may serve as a useful biomarker to identify GBM subpopulations with reduced stemness and increased immune engagement, offering potential therapeutic implications. Full article

The midgut of Antheraea pernyi plays a critical role in antiviral defense. However, its transcriptional complexity remains poorly understood. Here, a full-length (FL) transcriptome atlas of A. pernyi midgut was developed by integrating PacBio Iso-Seq and RNA-seq techniques. The transcriptome sequences included 1850 novel protein-coding genes, 17,736 novel alternative isoforms, 1664 novel long non-coding RNAs (lncRNAs), and 858 transcription factors (TFs). In addition, 2471 alternative splicing (AS) events and 3070 alternative polyadenylation (APA) sites were identified. Moreover, 3426 and 4796 differentially expressed genes (DEGs) and isoforms were identified after ApNPV infection, respectively, besides the differentially expressed lncRNAs (164), TFs (171), and novel isoforms of ApRelish (1) and ApSOCS2 (4). Enrichment analyses showed that KEGG pathways related to metabolism were suppressed, whereas GO terms related to DNA synthesis and replication were induced. Furthermore, the autophagy and apoptosis pathways were significantly enriched among the upregulated genes. Protein–protein interaction network (PPI) analysis revealed the coordinated downregulation of genes involved in mitochondrial ribosomes, V-type and F-type ATPases, and oxidative phosphorylation, indicating the disruption of host energy metabolism and organelle acidification. Moreover, coordinated upregulation of genes associated with cytoplasmic ribosomes was observed, suggesting that the infection by ApNPV interferes with host translational machinery. These results show that ApNPV infection reprograms energy metabolism, biosynthetic processes, and immune response in A. pernyi midgut. Our study provides a foundation for elucidating the mechanisms of A. pernyi–virus interactions, particularly how the viruses affect host defense strategies. Full article

Respiratory microbiota and lipids are closely associated with airway inflammation. This study aimed to analyze the correlations among the respiratory microbiome, the airway glycerophospholipid–sphingolipid profiles, and airway inflammation in patients with asthma. We conducted a cross-sectional study involving 61 patients with asthma and 17 healthy controls. Targeted phospholipidomics was performed on exhaled breath condensate (EBC) samples, and microbial composition was analyzed via the 16S rDNA sequencing of induced sputum. Asthma patients exhibited significant alterations in the EBC lipid profiles, with reduced levels of multiple ceramides (Cer) and glycerophospholipids, including phosphatidylethanolamine (PE) and phosphatidylcholine (PC), compared with healthy controls. These lipids were inversely correlated with the sputum interleukin-4 (IL-4) levels. Microbiome analysis revealed an increased abundance of Leptotrichia and Parasutterella in asthma patients, both positively associated with IL-4. Correlation analysis highlighted a potential interaction network involving PA, PE, ceramides, Streptococcus, Corynebacterium, Parasutterella, and Leptotrichia. Specific alterations in airway microbiota and phospholipid metabolism are associated with asthma-related inflammation, supporting the concept of a microbiota–phospholipid–immune axis and providing potential targets for future mechanistic and therapeutic studies. Full article

Background: Short-chain fatty acids (SCFAs), microbial metabolites also known as postbiotics, are essential for maintaining gut health. However, their antiproliferative effects on gastric cancer cells and potential interactions with conventional therapies remain underexplored. This study aimed to investigate the effects of three SCFA salts—magnesium acetate (A), sodium propionate (P), and sodium butyrate (B)—individually and in combination (APB), as well as in combination with dexamethasone (Dex), on AGS gastric adenocarcinoma cells. Methods: AGS cells were treated with PB, AP, AB, APB, Dex, and APB+Dex. Cell viability was assessed to determine antiproliferative effects, and the IC₅₀ of APB was calculated. Flow cytometry was used to evaluate apoptosis and necrosis. Reactive oxygen species (ROS) levels were measured to assess oxidative stress. Proteomic analysis via LC-MS was performed to identify differential protein expression and related pathways impacted by the treatments. Results: SCFA salts showed significant antiproliferative effects on AGS cells, with APB exhibiting a combined IC₅₀ of 568.33 μg/mL. The APB+Dex combination demonstrated strong synergy (combination index = 0.76) and significantly enhanced growth inhibition. Both APB and APB+Dex induced substantial apoptosis (p < 0.0001) with minimal necrosis. APB alone significantly increased ROS levels (p < 0.0001), while Dex moderated this effect in the combination group APB+Dex (p < 0.0001). Notably, the APB+Dex treatment synergistically targeted multiple tumour-promoting mechanisms, including the impairment of redox homeostasis through SLC7A11 suppression, and inhibition of the haemostasis, platelet activation network and NF-κB signalling pathway via downregulation of NFKB1 (−1.34), exemplified by increased expression of SERPINE1 (1.99) within the “Response to elevated platelet cytosolic Ca²⁺” pathway. Conclusions: These findings showed a multifaceted anticancer mechanism by APB+Dex that may collectively impair cell proliferation, survival signalling, immune modulation, and tumour microenvironment support in gastric cancer. Full article

Codonopsis pilosula polysaccharides have demonstrated multiple biological activities including immune regulation, antitumor, and antioxidant properties. The rapid development and integrated application of multi-omics can facilitate the unraveling of the complex network of immune system regulation. In this study, C. pilosula alkali-extracted polysaccharide (CPAP) were prepared, and their effects on gut microbiota compositions, metabolic pathways, and protein expressions in peripheral blood and solid tumors in mice were further evaluated. The 16S rDNA sequencing results showed that CPAP could effectively promote the enrichment of intestinal Lactobacillus in tumor-bearing mice. In addition, it could be inferred from peripheral blood and solid tumor proteomics results that CPAP might activate T cell-mediated antitumor immune functions by regulating purine metabolism and alleviate tumor-caused inflammation by promoting neutrophil degranulation, finally inducing apoptosis in tumor cells by increasing oxidative stress. These results will provide a theoretical foundation and data support for the further development of CPAP as dietary adjuvants targeting immune deficiency-related diseases. Full article

Metastatic breast cancer (BC) spread underscores the need for novel prognostic biomarkers. This study investigated CCR4-NOT Transcription Complex Subunit 7 (CNOT7) and leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) in BC progression and natural killer (NK) cell resistance. In the current study, 90 female BC patients (46 non-metastatic, 44 metastatic) were analyzed. CNOT7 and LAIR-1 protein levels were measured in serum via ELISA and CNOT7 expression in tissue by immunohistochemistry (IHC). In silico tools explored related pathways. Computational analyses, including in silico bioinformatics and molecular docking, explored gene functions, interactions, and ligand binding to CNOT7 and LAIR-1. CNOT7 serum levels were significantly elevated in metastatic patients (mean 4.710) versus non-metastatic patients (mean 3.229, p < 0.0001). Conversely, LAIR-1 serum levels were significantly lower in metastatic (mean 56.779) versus non-metastatic patients (mean 67.544, p < 0.0001). High CNOT7 was found in 50% (45/90) of cases, correlating with higher tumor grade, hormone receptor negativity, and increased lymph node involvement. Elevated CNOT7 and lower LAIR-1 levels were associated with worse overall survival. Pathway analysis linked CNOT7 to the PI3K/AKT/mTOR pathway. Computational findings elucidated CNOT7′s cellular roles, gene/protein interaction networks for LAIR-1/CNOT7, and distinct ligand binding profiles. High CNOT7 levels are associated with advanced BC stages and poor clinical outcomes, which suggests its utility as a prognostic biomarker. The inverse relationship between CNOT7 and LAIR-1 provides mechanistic insights into BC progression and immune evasion, further supported by in silico investigations. Full article

Background: Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), are genetically complex and often linked to structural genomic variations such as copy number variants (CNVs). Current diagnostic strategies face challenges in interpreting the clinical significance of such variants. Methods: We developed a customized, gene-oriented chromosomal microarray (CMA) targeting 6026 genes relevant to neurodevelopment, aiming to improve diagnostic yield and candidate gene prioritization. A total of 39 patients with unexplained developmental delay, intellectual disability, and/or ASD were analyzed using this custom platform. Systems biology approaches were employed for downstream interpretation, including protein–protein interaction networks, centrality measures, and tissue-specific functional module analysis. Results: Pathogenic or likely pathogenic CNVs were identified in 31% of cases (9/29). Network analyses revealed candidate genes with key topological properties, including central “hubs” (e.g., NPEPPS, PSMG1, DOCK8) and regulatory “bottlenecks” (e.g., SLC15A4, GLT1D1, TMEM132C). Tissue- and cell-type-specific network modeling demonstrated widespread gene involvement in both prenatal and postnatal developmental modules, with glial and astrocytic networks showing notable enrichment. Several novel CNV regions with high pathogenic potential were identified and linked to neurodevelopmental phenotypes in individual patient cases. Conclusions: Customized CMA offers enhanced detection of clinically relevant CNVs and provides a framework for prioritizing novel candidate genes based on biological network integration. This approach improves diagnostic accuracy in NDDs and identifies new targets for future functional and translational studies, highlighting the importance of glial involvement and immune-related pathways in neurodevelopmental pathology. Full article

Tumor drug resistance, a major cause of treatment failure, involves complex multi-gene networks, remodeling of signaling pathways, and interactions with the tumor microenvironment. Yin Yang 1 (YY1) is a critical oncogene overexpressed in many tumors and mediates multiple tumor-related processes, such as cell proliferation, metabolic reprogramming, immune evasion, and drug resistance. Notably, YY1 drives resistance through multiple mechanisms, such as upregulation of drug efflux, maintenance of cancer stemness, enhancement of DNA repair capacity, modulation of the tumor microenvironment, and epithelial–mesenchymal transition, thereby positioning it as a pivotal regulator of drug resistance. This review examines the pivotal role of YY1 in resistance, elucidating its molecular mechanisms and clinical relevance. We demonstrate that YY1 inhibition could effectively reverse drug resistance and restore therapeutic sensitivity across various treatment modalities. Importantly, we highlight the promising potential of YY1-targeted strategies, particularly combined with anti-tumor agents, to overcome resistance barriers. Furthermore, we discuss critical translational considerations for advancing these combinatorial approaches into clinical practice. Full article

Background and Objectives: A thorough comprehension of the essential molecules and related processes underlying the carcinogenesis, proliferation, and recurrence of acute myeloid leukemia (AML) is crucial. This study aimed to investigate the expression levels, diagnostic and prognostic significance and biological roles of Bcl-2-associated athanogene 4 (BAG4) in AML carcinogenesis. Materials and Methods: Gene expression profiles were analyzed using publicly available datasets, particularly GSE9476 and TCGA, using tools such as GEO2R, GEPIA2, UALCAN and TIMER2.0. The immune infiltration correlation was examined using the GSCA platform, while the function of BAG4 at the single-cell level was analyzed via CancerSEA. Protein–protein and gene–gene interaction networks were constructed using STRING and GeneMANIA, and enrichment analyses were performed using GO, KEGG and DAVID. Expression validation was performed using RT-qPCR in HL-60 (AML) and HaCaT (normal) cells, and ROC curve analysis evaluated the diagnostic accuracy. Results: BAG4 was significantly overexpressed in AML tissues and cell lines compared with healthy controls. High BAG4 expression was associated with poor overall survival and strong diagnostic power (AUC = 0.944). BAG4 was positively associated with immune cell infiltration and negatively associated with CD4+/CD8+ T and NK cells. At the single-cell level, BAG4 was associated with proliferation, invasion, and DNA repair functions. Functional network analysis showed that BAG4 interacted with apoptosis and necroptosis-related genes such as BCL2, BAG3 and TNFRSF1A and was enriched in pathways such as NF-κB, TNF signaling and apoptosis. Conclusions: BAG4 is overexpressed in AML and is associated with adverse clinical outcomes and immune modulation. It may play an important role in leukemogenesis by affecting apoptotic resistance and immune evasion. BAG4 has potential as a diagnostic biomarker and treatment target in AML, but further in vivo and clinical validation is needed. Full article

Understanding the interactions between specific cell types within tissue environments is essential for elucidating key biological processes, such as immune responses, cancer progression, inflammation, and development, in both physiological and pathological studies. The predominant methods for analyzing cell–cell interactions (CCI) rely primarily on statistical inference using mapping or network-based techniques. However, these approaches often struggle to prioritize meaningful interactions owing to the high sparsity and heterogeneity inherent in single-cell RNA sequencing (scRNA-seq) data, where small but biologically important differences can be easily overlooked. To overcome these limitations, we developed PriorCCI, a deep-learning framework that leverages a convolutional neural network (CNN) alongside Grad-CAM++, an explainable artificial intelligence algorithm. This study aims to provide a scalable, interpretable, and biologically meaningful framework for systematically identifying and prioritizing key ligand–receptor interactions between defined cell-type pairs from single-cell RNA-seq data, particularly in complex environments such as tumors. PriorCCI effectively prioritizes interactions between cancer and other cell types within the tumor microenvironment and accurately identifies biologically significant interactions related to angiogenesis. By providing a visual interpretation of gene-pair contributions, our approach enables robust inference of gene–gene interactions across distinct cell types from scRNA-seq data. Full article

Background: The prognosis management of thyroid cancer remains a significant challenge. This study highlights the critical role of T cells in the tumor microenvironment and aims to improve prognostic precision by integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data, providing a more comprehensive view of tumor biology at the single-cell level. Method: 15 thyroid cancer scRNA-seq samples were analyzed from GEO and 489 patients from TCGA. A multi-level attention graph neural network (MLA-GNN) model was applied to integrate T-cell-related differentially expressed genes (DEGs) for predicting disease-free survival (DFS). Patients were divided into training and validation cohorts in an 8:2 ratio. Result: We systematically characterized the immune microenvironment of metastatic thyroid cancer by using single-cell transcriptomics and identified the important role of T-cell subtypes in the development of thyroid cancer. T-cell-based DEGS between tumor tissues and normal tissues were also identified. Subsequently, T-cell-based risk signatures were selected for establishing a risk model using MLA-GNN. Finally, our MLA-GNN-based model demonstrated an excellent ability to predict the DFS of thyroid cancer patients (1-year AUC: 0.965, 3-years AUC: 0.979, and 5-years AUC: 0.949 in training groups, and 1-year AUC: 0.879, 3-years AUC: 0.804, and 5-years AUC: 0.804 in validation groups). Conclusions: Risk features based on T-cell genes have demonstrated the effectiveness in predicting the prognosis of thyroid cancer. By conducting a comprehensive characterization of T-cell features, we aim to enhance our understanding of the tumor’s response to immunotherapy and uncover new strategies for the treatment of cancer. Full article

The rapid evolution of pathogens and the limited genetic diversity of hosts are two major factors contributing to the plant pathogenic phenomenon known as the loss of disease resistance in maize (Zea mays L.). It has emerged as a significant biological stressor threatening the global food supplies and security. Based on previous cross-species homologous gene screening assays conducted in the laboratory, this study identified the maize disease-resistance candidate gene ZmNLR-7 to investigate the maize immune regulation mechanism against Bipolaris maydis. Subcellular localization assays confirmed that the ZmNLR-7 protein is localized in the plasma membrane and nucleus, and phylogenetic analysis revealed that it contains a conserved NB-ARC domain. Analysis of tissue expression patterns revealed that ZmNLR-7 was expressed in all maize tissues, with the highest expression level (5.11 times) exhibited in the leaves, and that its transcription level peaked at 11.92 times 48 h post Bipolaris maydis infection. Upon inoculating the ZmNLR-7 EMS mutants with Bipolaris maydis, the disease index was increased to 33.89 and 43.33, respectively, and the lesion expansion rate was higher than that in the wild type, indicating enhanced susceptibility to southern corn leaf blight. Physiological index measurements revealed a disturbance of ROS metabolism in ZmNLR-7 EMS mutants, with SOD activity decreased by approximately 30% and 55%, and POD activity decreased by 18% and 22%. Moreover, H₂O₂ content decreased, while lipid peroxide MDA accumulation increased. Transcriptomic analysis revealed a significant inhibition of the expression of the key genes NPR1 and ACS6 in the SA/ET signaling pathway and a decrease in the expression of disease-related genes ERF1 and PR1. This study established a new paradigm for the study of NLR protein-mediated plant immune mechanisms and provided target genes for molecular breeding of disease resistance in maize. Overall, these findings provide the first evidence that ZmNLR-7 confers resistance to southern corn leaf blight in maize by synergistically regulating ROS homeostasis, SA/ET signal transduction, and downstream defense gene expression networks. Full article

Rhabdomyosarcoma (RMS), the most common pediatric soft tissue sarcoma, comprises embryonal (ERMS) and alveolar (ARMS) subtypes with distinct histopathological features, clinical outcomes, and therapeutic responses. To better characterize their molecular distinctions, we performed untargeted plasma proteomics and metabolomics profiling in children with ERMS (n = 18), ARMS (n = 17), and matched healthy controls (n = 18). Differential expression, functional enrichment (GO, KEGG, RaMP-DB), co-expression network analysis (WGCNA/WMCNA), and multi-omics integration (DIABLO, MOFA) revealed distinct molecular signatures for each subtype. ARMS displayed elevated oncogenic and stemness-associated proteins (e.g., cyclin E1, FAP, myotrophin) and metabolites involved in lipid transport, fatty acid metabolism, and polyamine biosynthesis. In contrast, ERMS was enriched in immune-related and myogenic proteins (e.g., myosin-9, SAA2, S100A11) and metabolites linked to glutamate/glycine metabolism and redox homeostasis. Pathway analyses highlighted subtype-specific activation of PI3K-Akt and Hippo signaling in ARMS and immune and coagulation pathways in ERMS. Additionally, the proteomics and metabolomics datasets showed association with clinical parameters, including disease stage, lymph node involvement, and age, demonstrating clear molecular discrimination consistent with clinical observation. Co-expression networks and integrative analyses further reinforced these distinctions, uncovering coordinated protein–metabolite modules. Our findings reveal novel, subtype-specific molecular programs in RMS and propose candidate biomarkers and pathways that may guide precision diagnostics and therapeutic targeting in pediatric sarcomas. Full article

As a pivotal model organism in Lepidoptera research, the silkworm (Bombyx mori) holds significant importance in life science due to its economic value and biotechnological applications. Advancements in proteomics and bioinformatics have enabled substantial progress in characterizing the B. mori proteome. Systematic screening and identification of protein–protein interactions (PPIs) have progressively elucidated the molecular mechanisms governing key biological processes, including viral infection, immune regulation, and growth development. This review comprehensively summarizes traditional PPI detection techniques, such as yeast two-hybrid (Y2H) and immunoprecipitation (IP), alongside emerging methodologies such as mass spectrometry-based interactomics and artificial intelligence (AI)-driven PPI prediction. We critically analyze the strengths, limitations, and technological integration strategies for each approach, highlighting current field challenges. Furthermore, we elaborate on the molecular regulatory networks of Bombyx mori nucleopolyhedrovirus (BmNPV) from multiple perspectives: apoptosis and cell cycle regulation; viral protein invasion and trafficking; non-coding RNA-mediated modulation; metabolic reprogramming; and host immune evasion. These insights reveal the dynamic interplay between viral replication and host defense mechanisms. Collectively, this synthesis aims to provide a robust theoretical foundation and technical guidance for silkworm genetic improvement, infectious disease management, and the advancement of related biotechnological applications. Full article