Transcriptome analysis reveals the response mechanism of largemouth bass (Micropterus salmoides) brain to MSRV infection.

Imeglimin (IME) is a novel oral anti-diabetic agent with a similar chemical structure to metformin, which has shown broad-spectrum anti-tumor activity. However, the activity of imeglimin on tumor cells remains unclear. This study investigated the effects of IME on multiple myeloma (MM) cells and explored the underlying mechanisms. The effects of IME on MM cell proliferation were evaluated invitro using MM cell lines and in MM cell-derived xenograft (CDX) models. Seahorse metabolic analyses and RNA-Seq were performed in IME-treated and control MM cell lines. Single-cell transcriptomic data were further analyzed to assess the role of IL-16 in the bone marrow microenvironment. IME inhibited MM cell proliferation and tumor growth in MM cell-derived xenograft (CDX) models by inducing G1/G0 cell cycle arrest. IME suppressed oxidative phosphorylation and promoted glycolysis. IL-16 mRNA expression was downregulated, and multiple cytokine-cytokine receptor interaction pathways were altered following IME treatment. The anti-MM effect of IME was partly mediated by increased lactate production and decreased IL-16 expression. Single-cell transcriptomic data further demonstrated that IL-16 plays an important role in the bone marrow microenvironment of MM. These findings suggest that IME may represent a novel approach for targeting IL-16 and energy metabolism in the treatment of MM.

Integrated miRNA and mRNA analysis in gills of Amur ide (Leuciscus waleckii) reveals novel insights into the molecular regulatory mechanism of alkaline acclimation.

African swine fever (ASF) is a highly fatal disease caused by the African swine fever virus (ASFV). It infects domestic pigs and wild boars, causing significant economic losses worldwide. However, effective vaccines against this virus remain not be commercialized because of its large genome and high mutation frequency. Thus, antiviral therapies need to be developed urgently. Chloroquine phosphate (CQP) has been demonstrated in previous studies to exert inhibitory effects against a variety of viruses, but its inhibitory effect against the SY-1 strain of ASFV remains unclear. Therefore, we selected CQP as the research subject to investigate its anti-ASFV function. In this study, we confirmed that CQP has a significant inhibitory effect on the ASFV SY-1 strain by RT-qPCR, Western Blot, and HAD50. Transcriptome sequencing and KEGG pathway enrichment analysis showed that CQP treatment significantly affected multiple signaling pathways, including the cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, tumour necrosis factor (TNF) signaling pathway and IL-17 signaling pathway. Western Blot results further indicated that CQP can inhibit ERK phosphorylation. Treatment with the MAPK agonist C16-PAF reversed the inhibitory effect of CQP, verifying the key role of this pathway in the anti-viral mechanism of CQP. In sum, the results of this study indicate that CQP effectively inhibits ASFV replication by suppressing the MAPK-ERK signaling pathway. This study provides a theoretical basis and technical support for the development of anti-viral strategies targeting ASFV.

To identify key genes and inflammatory signaling pathways involved in the anti-inflammatory effects of Hedysarum polybotrys polysaccharide (HPS) in a rat model of endotoxin-induced uveitis (EIU). EIU was induced in Wistar rats through subcutaneous injection of lipopolysaccharide (LPS, 200 µg) and the rats were then randomly assigned to EIU group (n=5) and the HPS intervention group (n=5). HPS (400 mg/kg, intraperitoneally) or its carrier was administered 24h and 1h prior to EIU induction. Eyes were examined and enucleated 24h post-induction, and total RNA was extracted from the iris-ciliary body. Gene expression microarrays were used to identify differentially expressed genes (DEGs), followed by bioinformatics analyses, including gene ontology (GO) and pathway analysis. Key findings were not experimentally validated at the mRNA or protein level. A total of 322 DEGs were identified, comprising 254 mRNA and 68 lncRNA genes. GO analysis revealed significant functional categories, including response to LPS. Pathway analysis identified key signaling pathways involved in uveitis, such as cytokine-cytokine receptor interactions. Notably, 16 mRNA and 7 lncRNA DEGs emerged as central nodes in the gene correlation network. HPS exerts its anti-inflammatory effects through coordinated signaling pathways, offering insights into potential therapeutic targets for managing uveitis.

Endocrine-disrupting chemicals (EDCs) are common environmental pollutants that affect human health. These chemicals have been linked to the development and progression of many types of cancer. However, the mechanism by which EDCs affect the development of lung adenocarcinoma (LUAD) remains unclear. Bioinformatics and machine learning approaches were used to explore the connection between 13 EDCs and LUAD. Target genes for the 13 EDCs were retrieved from the ChEMBL, STITCH, and SwissTargetPrediction databases. Differentially expressed genes (DEGs) in LUAD were identified from GEO datasets. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks were used to identify key gene modules related to LUAD. Machine learning methods were employed to select important genes associated with both LUAD and EDC exposure. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to assess relevant biological pathways. Immune cell infiltration was analyzed using CIBERSORT to explore changes in the immune microenvironment of LUAD. A total of 1,818 putative target genes for the 13 EDCs were initially identified. WGCNA refined this list to 93 candidate genes, among which COL1A1 was prioritized as a robust feature gene via machine learning algorithms. Functional enrichment analysis indicated that COL1A1 is involved in critical pathways, including cytokine-cytokine receptor interaction, ECM-receptor interaction, and fatty acid metabolism. Furthermore, immune infiltration profiling revealed distinct correlation patterns: COL1A1 expression was positively correlated with the abundance of M0 and M2 macrophages, while showing a negative correlation with monocytes and eosinophils. This study identifies COL1A1 as a key feature gene associated with EDC-targeted pathways in LUAD. The findings characterize its involvement in ECM-related signaling and its correlation with specific immune cell infiltration patterns, providing a computational framework for understanding EDC-associated molecular changes in LUAD.

Chromobox protein-7 (CBX7) functions as a gene repressor. We, unexpectedly, found that CBX7 formed a methylation-dependent transcriptional complex, which induced gene transcription by binding to cytokine gene promoters. CBX7 translocated to the cytosol and formed a methylation-dependent signaling complex with c-Raf, MAPK (mitogen-activated protein kinase) kinase 1/2 (MEK1/2), and casein kinase-2 (CK2)–α to generate and sustain extracellular signal–regulated kinase 1/2 (ERK1/2) signaling. CBX7 is an allergen-inducible gene. Genetic and pharmacologic interventions established an essential role for CBX7 for the production of cytokines by mouse and asthmatic patient lymphoid cells, and for the induction of allergic asthma in multiple mouse models. RNA sequencing demonstrated a large-scale loss and gain in gene transcription in Cbx7−/− T cells. The top down-regulated pathways included cytokine-cytokine receptor interaction, asthma, and T helper cell differentiation. CBX7 induction of the transcriptional activation complex and methylation of the ERK1/2 signalosome was specific for lymphoid cells as they were absent in epithelial cells. Our studies established a previously unknown paradigm of CBX7-generated methylation-dependent signaling complexes regulating inflammation.

Cancer-associated secondary lymphedema (CASL) commonly occurs after tumor-related lymph node dissection and radiotherapy. Nevertheless, the mechanisms of CASL remain unclear, and there are no specific molecular markers for its diagnosis and treatment. In this study, RNA sequencing was performed on adipose tissues from 10 normal controls and 40 patients with CASL. Differentially expressed genes were screened using two machine learning algorithms to identify potential molecular markers for CASL. Subsequently, seven machine learning algorithms were employed to develop predictive models based on the identified markers. The contribution of each feature to the predictive outcomes was interpreted using Shapley additive explanation (SHAP). Immune cell infiltration was profiled through CIBERSORT and MCP-counter algorithms, and single-cell RNA sequencing (scRNA-seq) data were integrated to explore interactions between characteristic genes and immune cell subpopulations. Furthermore, associations between characteristic genes and clinical parameters were also assessed. IL2RG, HOXD10, and TSPAN1 were identified as potential biomarkers of CASL. Diagnostic models built on these three genes showed excellent performance. Functional enrichment analysis suggested that the dysregulation of cytokine-cytokine receptor interactions and immune pathways underlies the pathological progression of CASL. In addition, immune infiltration analysis indicated that T cell and macrophage infiltration were intricately involved in CASL progression. Intriguingly, single-cell transcriptomic analysis further revealed elevated expression of IL2RG, HOXD10, and TSPAN1 in T cell subsets. Finally, RT-qPCR validated that these genes were expressed at higher levels in CASL tissues than in normal tissues. Moreover, IL2RG expression was strongly associated with clinical parameters. This study identified IL2RG, HOXD10, and TSPAN1 as novel potential molecular markers for CASL, providing valuable biological evidence for the diagnosis and intervention of CASL.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by persistent mucosal inflammation. While fibroblasts contribute to tissue repair and fibrosis, their role in UC pathogenesis remains incompletely understood. Single-cell RNA sequencing (GSE114374, GSE231993) and cell-cell communication analysis were performed to profile fibroblast interactions. Weighted Gene Co-expression Network Analysis (WGCNA) identified fibroblast-related modules. Machine learning models predicted diagnostic biomarkers. IF, ELISA and WB assays were used to validate the expression of ANGPTL2 in UC samples. In vivo validation using a DSS-induced UC mouse model evaluated ANGPTL2 expression and therapeutic response to GDC-0152. Spatial transcriptomics (GSE189184) was used to analyze tissue regionalization, cell-type spatial distribution, and the localized expression of ANGPTL2 and its colocalization with fibroblasts. Single-cell analysis identified fibroblasts as a key cell type in UC. WGCNA revealed a strong correlation between the turquoise module and fibroblast infiltration, with genes in this module enriched in pathways such as cytokine-cytokine receptor interaction and TNF signaling. Machine learning models identified PHLDA1 and ANGPTL2 as potential biomarkers with high diagnostic accuracy (AUC 0.935 and 0.917, respectively). Furthermore, exprimental analyses confirmed that ANGPTL2 was significantly upregulated in fibroblasts from UC patients, serum ELISA experiments demonstrated that ANGPTL2 was also significantly elevated in the serum of UC patients, supporting its potential as a non-invasive diagnostic indicator. In vivo experiments confirmed ANGPTL2 upregulation in DSS-treated mice, reversed by GDC-0152. Spatial transcriptomics partitioned UC tissue into 13 distinct regions, revealing prominent fibroblast distribution and markedly high ANGPTL2 expression with significant spatial colocalization with fibroblasts. Fibroblasts are central to UC pathogenesis, contributing to inflammation and fibrosis. ANGPTL2 is a promising diagnostic biomarker and potential therapeutic target in UC.

Ingestion of a human-relevant mixture of environmentally sourced microplastics promotes inflammation and tumorigenesis in the mouse colon.

Transcriptomic landscapes of the endometrium of dairy cows with clinical or subclinical endometritis.

Introduction: Ischemic stroke initiates a cascade of molecular events including increased neuroinflammation, immune dysregulation, and vascular injury, yet the biological mechanisms of post-stroke recovery remain poorly understood. In both experimental and clinical studies, social isolation (SI) impairs stroke recovery through distinct immunometabolism and neuroendocrine pathways, and these may differ by sex. Proteomic integration with psychosocial and biological variables has the potential to reveal mechanisms driving stroke disparities and inform targeted interventions. Hypothesis: We hypothesize that stroke triggers sex- and isolation-specific alterations in immune, vascular, and metabolic pathways that drive divergent post-stroke outcomes. Methods: We analyzed plasma proteomic profiling via Single-Run LC-MS/MS in ischemic stroke patients (N=154, 63±14 y, 59 females, 95 males) 24 hours after last known well. Samples were collected from imaging-confirmed strokes. Isolation was measured via the Lubben Social Network Scale and loneliness with the UCLA Loneliness Scale during hospitalization. Results: In males, proteins associated with higher UCLA loneliness scores were significantly enriched for pathways related to coagulation, complement activation, and vascular inflammation. The top-ranking KEGG terms included “Complement and coagulation cascades,” “Platelet activation,” and “Neuroactive ligand receptor interaction.” The enrichment of complement and coagulation pathways reflects systemic thrombo-inflammatory activation, which has been mechanistically linked to blood-brain barrier disruption, microglial priming, and neurovascular injury. In females, loneliness-associated proteins also converged on immune and vascular regulatory pathways, although with distinct signatures compared to males. The top enriched KEGG pathways included “Complement and coagulation cascades,” “Lipid and atherosclerosis,” and “Cytokine-cytokine receptor intera ction.” The prominence of complement proteins such as C3, C4A, and C4B reinforces the role of classical complement activation in shaping neuroimmune responses that influence mood and social behavior. Conclusion: These findings establish that sex and SI fundamentally reprogram post-stroke proteomic landscapes. Defining these context-specific responses offers a path toward precision therapies that address sex differences and mitigate the long-term burden of stroke and social isolation across populations.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder with unclear molecular mechanisms, complicating early diagnosis and treatment. This study aimed to identify hub genes and pathways driving RA pathogenesis and assess their therapeutic potential. Gene expression datasets related to RA were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and analyzed by functional enrichment and protein-protein interaction network construction. Machine learning approaches, including LASSO regression, random forest, and SVM-RFE, were used to screen hub genes. Pathway associations were explored using Gene Set Enrichment Analysis (GSEA). Experimental validation was performed in collagen-induced arthritis (CIA) rat models and MH7A synovial fibroblast cells through Western blot and functional assays. A total of 106 DEGs were identified in RA synovial tissues, including 76 upregulated and 30 downregulated genes. Enrichment analyses revealed involvement in cytokine-cytokine receptor interaction, lymphocyte-mediated immunity, and immunoglobulin complexes. SDC1 emerged as a key hub gene across all three machine learning methods. GSEA indicated its significant correlation with the JAK-STAT pathway. In CIA rats, SDC1 expression was markedly elevated alongside p-JAK2 and p-STAT3 levels. Silencing SDC1 in MH7A cells reduced cell proliferation, decreased p-JAK2 and p-STAT3 expression, and promoted apoptosis. This study identifies SDC1 as a central hub gene in RA pathogenesis through activation of the JAK2-STAT3 signaling pathway. These findings highlight SDC1 as a potential biomarker for early diagnosis and a promising target for therapeutic intervention, providing new insights into RA management.

Atherosclerosis (AS) serves as the pathological foundation for numerous cardiovascular and cerebrovascular diseases and is highly comorbid with depression. The mechanisms underlying this co-morbidity are exceptionally complex, posing significant challenges to effective clinical treatment. Consequently, our study aims to explore the potential biomarkers and mechanisms involved in developing atherosclerosis co-depression disease. We performed differential expression analysis, protein-protein interaction analysis, Gene Ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on co-differentiated genes using AS and depression-related datasets from the GEO database. Potential biomarkers were identified through ROC curve analysis. To evaluate the effectiveness of the model, we established an animal model of AS comorbid with depressive disorder and performed a series of assessments, including the sugar-water preference test, open field test, tail suspension test, lipid profile analysis, and pathological examination of aortic sections. Additionally, RNA sequencing analysis of brain tissue, Golgi staining, and detection of synaptic function-related proteins were performed in AS comorbid depressed mice. Finally, in vitro cellular experiments were conducted to further validate the molecular targets and underlying mechanisms. We identified 968 differentially expressed genes associated with AS and 472 differentially expressed genes associated with depression, with 30 genes co-differentially expressed. Protein-protein interaction (PPI) analysis revealed that CCR5, CCR2, NPY, and OPRM1 were strongly associated with AS co-depression, while ROC analysis indicated that Shank2, MDGA2, and S100B were diagnostic markers for AS with depression. Differentially expressed genes were closely associated with the chemokine signaling pathway, neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction, and taste transduction. Animal studies demonstrated that ApoE-/- mice exhibited elevated lipids, atherosclerotic plaques, and depressive behaviors. RNA sequencing revealed that 30 genes were differentially expressed in the high-fat combined bind (HFB) group, with inflammatory pathways also activated in the brain. Shank2 expression was decreased in the hippocampus and prefrontal cortex, and Golgi staining revealed impaired synaptic plasticity. Cellular experiments demonstrated that IL-1β could induce a decrease in Shank2 expression. Our study identified seven candidate AS co-depression biomarkers and verified that inflammation-induced damage to synaptic plastic rows is an important mechanism of AS co-depression, providing new insights into the diagnosis and treatment of AS co-depression disorders.

A novel mechanism of Reduning injection in sepsis treatment: Targeting inflammatory kinases TBK1 and IKKβ.

Temporal dynamics of insulin-responsive long non-coding RNAs in chicken skeletal muscle: unraveling LncFKBP5 as a dual regulator of myogenesis and glucose homeostasis.

The impact of the phoP gene on Pseudomonas plecoglossicida virulence and host immune responses in Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂.

Comprehensive transcriptome profiling of the chicken bursa infected with the novel variant infectious bursal disease virus.

Impact of low and high lipopolysaccharide doses on the early expression of immune related genes and transcription factors in chicken macrophage-like HD11 cells.

The main active component Kaji-ichigoside F1 of the ethnic medicine Rosa roxburghii Tratt prevents acetaminophen-induced acute liver injury by modulating microbial metabolism.