搜索结果：Immune network

This study integrated network pharmacology, bioinformatics, and molecular docking to explore potential immune-inflammatory pathways associated with the relationship between gut microbiota-derived metabolites and Alzheimer's disease (AD). A total of 260 gut microbiota - derived metabolites were initially retrieved, and 196 common targets were identified by intersecting predicted metabolite-associated targets with AD-related targets. Further screening identified 14 key overlapping targets, including IL6, NFKB1, IL1B, PTGS2, TLR4, and PPARG. Protein-protein interaction (PPI) network analysis identified IL6, NFKB1, IL1B, CXCL8, PPARG, FOS, and JUN as central hub genes. Functional enrichment analyses indicated that these targets were mainly involved in immune-inflammatory responses, response to lipopolysaccharide, oxidative stress-related processes, and regulation of apoptosis. KEGG pathway analysis further suggested that the overlapping targets were associated with several inflammation-related signaling pathways, including the NOD-like receptor, TNF, NF-κB, and MAPK signaling pathways. In silico pharmacokinetic and toxicity evaluation showed that several representative metabolites exhibited heterogeneous but informative drug-likeness and pharmacokinetic/toxicity-related profiles relevant to gut-brain-axis hypothesis generation. Molecular docking was performed as an exploratory structural assessment and suggested that selected metabolites, including Enterodiol, Coumarin, and 3,9-dihydroxy-6H-benzo[c]chromen-6-one, showed top-ranked predicted docking poses in computationally identified surface-accessible pockets of representative hub proteins such as IL6 and NFKB1, with docking scores ranging from - 6.8 to - 8.1 kcal/mol. These docking scores were interpreted only as qualitative descriptors of predicted structural compatibility and were not used to infer quantitative biological activity, target inhibition, or therapeutic efficacy. Overall, this study prioritizes a potential multi-target immune-inflammatory network centered on IL6, NFKB1, and IL1B, providing a hypothesis-generating framework for understanding the possible role of gut microbiota-derived metabolites in AD-related neuroimmune regulation. Further experimental studies are required to validate the predicted metabolite-target associations and clarify their biological relevance.

Osteoarthritis (OA) has traditionally been viewed as a degenerative joint disorder primarily associated with mechanical stress and progressive structural damage. Increasing evidence, however, indicates that immune imbalance and persistent low-grade inflammation are critically involved in disease initiation and progression. In this context, alterations in the gut microbiota have attracted growing attention due to their capacity to influence systemic immune responses. Here, we provide an integrated overview of the interactions between the gut microbiota and the immune system in OA and introduce the concept of a "gut microbiota-immune-joint axis" to describe this interconnected regulatory network. Disruption of the gut microbial ecosystem may impair intestinal barrier function and facilitate the entry of microbe-derived signals into the circulation. These signals subsequently activate inflammatory pathways, including TLR4/NF-κB and JAK/STAT cascades, leading to immune cell reprogramming, altered macrophage polarization, and imbalanced T-cell responses. The resulting chronic inflammatory state can extend beyond the intestine and contribute to pathological changes in synovial tissue, cartilage, and subchondral bone. In addition, we summarize current progress in microbiota-oriented therapeutic strategies, particularly the use of probiotics and prebiotics, and discuss their potential roles in modulating immune responses and restoring systemic homeostasis. Finally, we highlight existing challenges and propose future directions, emphasizing the importance of multi-omics integration and longitudinal clinical studies to better understand the dynamic nature of microbiota-immune interactions and to support the development of targeted interventions in OA.

Atherosclerosis (AS) is driven by intertwined inflammatory responses and vascular wall remodeling, yet the core regulatory networks and actionable targets underlying plaque formation remain incompletely defined. Bulk transcriptomic data from GSE43292, comprising paired advanced carotid plaques and distant early-stage lesion tissues from 32 patients, were analyzed to identify differentially expressed genes (DEGs), followed by WGCNA and intersection with GeneCards-derived AS-related genes. Candidate genes were mapped to a STRING PPI network, and hub genes were prioritized using four Cytoscape algorithms (MCC, EPC, Stress, Degree). Functional annotation (GO/KEGG), immune infiltration, and single-gene GSEA were performed. A 10 × scRNA-seq dataset (GSE159677) was used for cell-type annotation, compositional comparison, cell-cell communication, and hub-gene localization. Drug prediction was conducted, followed by molecular docking of candidate compounds with hub proteins. Finally, an oxLDL-induced in vitro model was used for validation by CCK-8, ROS staining, Western blotting, and qRT-PCR. Seven hub genes (SMAD4, CASP8, PARP1, CRKL, CDK6, VDAC1, KHDRBS1) were identified. Enrichment analyses linked these hubs to cell death/stress regulation, immune-related programs, and vascular remodeling. Immune infiltration suggested marked immune reconfiguration in plaques. Single-gene GSEA highlighted coordinated remodeling of vascular smooth muscle contraction, gap junction signaling, and lipid/NAD-associated metabolism. scRNA-seq analysis indicated joint contributions from myeloid and vascular structural cells, with hub genes showing cell-type-biased enrichment. Quercetin emerged as a candidate compound; docking supported favorable multi-target binding (strongest for PARP1 and CDK6). Experimentally, oxLDL upregulated hub-gene mRNA/protein levels, while quercetin significantly attenuated these increases. We define an AS-associated hub-gene network with single-cell context and provide convergent computational and experimental evidence that quercetin exerts endothelial-protective effects consistent with multi-target modulation of the hub-gene network, supporting its therapeutic potential in AS.

This study aimed to explore the exosome-related gene network in herpes stromal keratitis (HSK) and to identify key molecular drivers, focusing on the role of secreted phosphoprotein 1 (SPP1) and investigating the therapeutic potential of ursolic acid (UA). Transcriptomic RNA sequencing was performed on corneal tissues from herpes simplex virus type 1 (HSV-1)-infected mice. Bioinformatics analyses included identification of differentially expressed genes (DEGs), immune cell infiltration assessment, exosome-related gene network construction, functional enrichment analyses, and compound-gene network creation. Immune infiltration findings were further validated using single-cell RNA sequencing and flow cytometry. Key findings were validated in a mouse HSK model using quantitative real-time polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry. The therapeutic effect and mechanism of UA were investigated through subconjunctival administration, with subsequent evaluation of corneal lesions, angiogenesis, fibrosis, and key signaling pathways. Through analysis, 222 DEGs associated with exosomes were identified in HSK, and the exosome-related gene network revealed that the primary functional cluster was centered on the positive regulation of cell adhesion. Among the genes in the primary functional cluster, SPP1 emerged as a hub gene, demonstrating significant upregulation that correlated with clinical progression. SPP1 expression exhibited a shift from corneal epithelial cells under normal conditions to infiltrating immune cells, particularly neutrophils and monocytes, during HSK. In a mouse model, treatment with UA significantly reduced corneal opacity, vessel in-growth, and inflammatory cell infiltration. Mechanistically, UA downregulated SPP1 expression and subsequently inhibited the activation of the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway. These findings provide new insights into the exosome-related gene network and the mechanisms of HSK, while identifying UA as a promising candidate for therapeutic development.

Decabromodiphenyl ether (BDE-209) is a widely used flame retardant and persistent environmental contaminant. However, the overlap between predicted BDE-209-related targets and ulcerative colitis (UC)-associated molecular signatures has not been systematically evaluated. Public UC transcriptomic datasets and predicted BDE-209-related targets were integrated for comparative bioinformatic analysis. Differential expression analysis, enrichment analysis, machine learning algorithms, protein-protein interaction network analysis, immune infiltration analysis, single-cell RNA sequencing analysis, and molecular docking were performed to prioritize candidate genes associated with BDE-209-related targets and UC-associated signatures. A total of 87 overlapping genes were identified between predicted BDE-209-related targets and UC-associated differentially expressed genes. Functional enrichment analysis showed that these genes were mainly enriched in inflammation- and immune-related pathways. Machine learning analysis and protein-protein interaction network analysis prioritized eight candidate hub genes (IL1B, MMP9, ICAM1, PPARG, VCAM1, AREG, FABP3, and ACSL5). Immune infiltration and single-cell transcriptomic analyses showed that these genes were mainly expressed in immune-related cell populations within UC datasets. Molecular docking analysis suggested potential structural compatibility between BDE-209 and the protein structures encoded by the identified hub genes. This study identified shared molecular features between predicted BDE-209-related targets and UC-associated transcriptomic signatures. These findings provide a hypothesis-generating resource for future studies investigating the potential relevance of environmental contaminant-related target networks in UC-associated inflammatory contexts.

N6-methyladenosine (m6A) is the most abundant internal modification of eukaryotic mRNA, and its dysregulation is increasingly linked to autoimmune diseases. Unlike previous reviews that broadly cover the entire m6A network, this review adopts an m6A writer centric perspective. The m6A writer complex serves as the upstream gatekeeper of epitranscriptomic regulation. We summarize its structural organization and cell‑type‑specific functions in controlling immune cell differentiation and function, as well as its protective roles in parenchymal tissues. Accumulating evidence shows that the writer complex exerts context‑dependent dual actions across a wide range of autoimmune conditions, including rheumatoid arthritis, lupus, psoriasis, and others. We also discuss the mechanistic basis of this functional heterogeneity, involving microenvironmental signals, subunit composition, and reader proteins. Notably, our review not only focuses on the roles and mechanisms of writer‑mediated m6A modification in various immune cells but also summarizes its pharmacological modulators, which represents a key advantage over previous reviews. By focusing specifically on the writer complex, we provide a more proximal and mechanistically feasible framework for precise epitranscriptomic intervention in autoimmune disorders.

Hepatocellular carcinoma (HCC) poses a significant global health burden with limited therapeutic options, particularly for non-viral etiologies. The mitochondrial solute carrier SLC25A43 is implicated in cellular redox homeostasis, yet its role in HCC remains unclear. This study aimed to comprehensively investigate the expression pattern, clinical significance, biological function, and potential mechanisms of SLC25A43 in HCC. Utilizing multi-omics data from public databases (TCGA-LIHC, GEO, and HPA), we performed integrated bioinformatic analyses. SLC25A43 was consistently upregulated in HCC tissues compared with non-tumorous liver tissues and demonstrated strong diagnostic value (AUC = 0.861). High SLC25A43 expression was significantly associated with advanced tumor stage, metastasis, and adverse clinicopathological features. Survival analyses identified SLC25A43 as an independent prognostic risk factor for overall survival, progression-free interval, and disease-specific survival. Functional enrichment analyses suggested that SLC25A43 is involved in mitochondrial oxidative phosphorylation, energy metabolism, and immune-related pathways. Immune infiltration analyses using ssGSEA, xCell, and TIMER consistently revealed negative correlations between SLC25A43 expression and multiple antitumor immune cell populations, particularly CD8 + T cells. Experimental validation confirmed that SLC25A43 was significantly upregulated in HCC tissues at both mRNA and protein levels. Functional assays in Huh-7, Hep-LM3, MHCC97H, and LO2 cells demonstrated that SLC25A43 knockdown inhibited, whereas overexpression promoted, cell proliferation and migration. Rescue experiments further verified the specificity of these effects. Mechanistically, SLC25A43 regulated intracellular ATP production, ROS accumulation, and glutathione metabolism, indicating a role in redox homeostasis and energy metabolism. In addition, PBMC co-culture experiments showed that SLC25A43 suppressed CD8 + T-cell cytotoxic activity by reducing Granzyme B expression. A prognostic nomogram incorporating SLC25A43 exhibited favorable predictive performance and was successfully validated in two independent GEO cohorts. SLC25A43 is a novel diagnostic and prognostic biomarker for HCC. Its upregulation promotes tumor progression through metabolic reprogramming, redox homeostasis remodeling, and suppression of antitumor immune responses. These findings highlight SLC25A43 as a promising therapeutic target and provide new insights into the metabolic-immune regulatory network in hepatocellular carcinoma.

Immune cell states are not fixed. Rather, they emerge from dynamic transcriptional programs shaped by genetic variation, cellular context, and gene regulatory networks (GRNs). Single-cell and multi-omic technologies now enable population-scale immune profiling across molecular layers, revealing that cell-to-cell transcriptional variability is a functional feature that diversifies immune responses. Distribution-aware and tensor-based analytical frameworks capture this variability beyond mean expression, resolving coordinated gene programs across cell types, individuals, and conditions. Integrating human genetics with single-cell genomics demonstrates that genetic effects on gene expression, splicing, and chromatin accessibility are highly dependent on cell type, activation state, and differentiation trajectory. These variant-level signals converge on GRNs in which key transcription factors orchestrate context-dependent immune programs. High-throughput perturbation screens enable scalable functional validation of these networks, linking genetic variation to cellular function. Together, these integrative approaches translate molecular discoveries into clinical applications, from patient stratification to therapeutic target prioritization. Emerging spatial multi-omics and in situ perturbation screens further resolve neighbor-dependent regulation within intact tissue niches, offering a path from variant to mechanism to clinical translation.

Neuroblastoma is the most common extracranial solid malignancy in children and accounts for nearly 15% of paediatric cancer-related mortality, underscoring its substantial clinical burden. Although multimodal therapeutic strategies, including chemotherapy, surgical resection, radiotherapy, stem cell transplantation, and immunotherapy, have improved outcomes in low- and intermediate-risk disease, survival rates for high-risk neuroblastoma remain poor due to frequent relapse and treatment resistance. While oncogenic drivers, such as MYCN amplification and ALK mutations have been extensively investigated, accumulating genomic and epigenomic evidence indicates that disruption of tumor suppressor gene (TSG) networks plays a central role in neuroblastoma pathogenesis. Unlike many adult malignancies driven by somatic mutations, neuroblastoma frequently exhibits tumor suppressor gene dysfunction through chromosomal deletions, copy number alterations, epigenetic silencing, and dysregulated signaling pathways. Major tumor suppressive pathways affected include Tp53-mediated apoptosis and genomic stability, RB-dependent cell cycle regulation, PTEN/PI3K/AKT survival signaling, Hippo pathway control of proliferation and stemness, and DNA damage response mechanisms. These interconnected networks drive tumor progression, metastatic dissemination, immune evasion, metabolic adaptation, and therapeutic resistance. Consequently, researchers are actively exploring therapeutic strategies targeting tumor suppressor-associated vulnerabilities. However, clinical translation remains challenging due to tumor heterogeneity, developmental toxicity concerns, and adaptive resistance mechanisms. This review summarizes the molecular mechanisms underlying tumor suppressor dysfunction in neuroblastoma and discusses emerging translational strategies targeting interconnected oncogenic, epigenetic, metabolic, and immune-associated signaling networks.

Orthodontic tooth movement (OTM) is a dynamic biological process driven by mechanically induced tissue remodeling. The transcriptional regulators, particularly the noncoding RNAs in human tissues remains limited. This study employs RNA sequencing and bioinformatics to analyze the transcriptomic profile at day 7 of OTM, and to identify potential regulatory factors associated with mechanical stimulus. RNA sequencing was performed on human periodontal ligament tissues obtained from extracted first premolars of OTM and non-OTM (control) patients (n = 3 per group) after seven days of orthodontic force application. Differentially expressed mRNAs, lncRNAs, and circRNAs were identified, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Competing endogenous RNAs (ceRNA) networks and a mechanical stimulus-related circRNA-miRNA-mRNA network were constructed using bioinformatic approaches. qRT-PCR was applied for validation in an independent set of samples (n = 5 per group). Compared with control group, a total of 337 mRNAs, 687 lncRNAs, and 377 circRNAs were significantly up-regulated in human periodontal ligament tissues of the OTM group. Conversely, 341 mRNAs, 1253 lncRNAs, and 109 circRNAs exhibited down-regulated expression during OTM. GO and KEGG analyses highlighted key biological functions and signaling pathways involved in bone modeling, with significant enrichment of inflammatory and immune-effector pathways. A predicted lncRNA/circRNA-miRNA-mRNA network active during OTM was constructed, and the circELP3-hsa-miR-629-3p/hsa-miR-29b-2-5p-TLR7/ITGAM axis was identified as potential regulatory pathway related to mechanical stimulus. This finding was subsequently validated by qRT-PCR. mRNAs, lncRNAs, and circRNAs in human periodontal tissues are differentially expressed at day 7 of OTM, and form a complex ceRNA network. The circELP3-hsa-miR-629-3p/hsa-miR-29b-2-5p-TLR7/ITGAM axis might be involved in mechanical stimulus of OTM at day 7. These findings provide insights and molecular clues that could inform future strategies for modulating bone remodeling in periodontal tissues during orthodontic treatment.

Macrophages are pivotal effector cells within the innate immune system, playing a central role in inflammation regulation, tissue homeostasis, and immune defense. Recent studies have demonstrated that macrophage autophagy-a highly conserved process essential for cellular homeostasis-plays a critical role in dynamically balancing immune responses by selectively eliminating damaged organelles, pathogens, and protein aggregates. Macrophage autophagy is finely regulated by various signaling pathways, such as mTOR and NF-κB, and its dysfunction is closely associated with the onset and progression of allergic diseases. This review systematically synthesizes the molecular mechanisms governing macrophage autophagy and its dual role in allergic diseases, including allergic rhinitis, asthma, and atopic dermatitis. It highlights the functions of key signaling pathways (e.g., mTOR, NF-κB) and regulatory factors (e.g., p62, Beclin-1, LC3) and explores the crosstalk between macrophage autophagy, immunometabolism, and cellular polarization. Explores the crosstalk between macrophage autophagy, immunometabolism, and cellular polarization, and further elaborates the reciprocal regulatory network of autophagy and metabolic reprogramming within allergic inflammatory microenvironment. Furthermore, the review summarizes potential therapeutic strategies targeting macrophage autophagy, such as budesonide/simvastatin combination therapy and rapamycin derivatives, along with their clinical translation prospects, with the aim of providing a theoretical foundation for developing novel, autophagy-targeted precision therapies for allergic diseases.

Brain metastasis remains a major clinical challenge because intracranial progression and therapeutic resistance arise not only from tumour-intrinsic programmes but also from coordinated communication across molecular, cellular, spatial and systemic scales. In this Review, we integrate findings from spatial transcriptomics/proteomics, single-cell and multimodal omics, and computational systems modelling to frame brain metastasis as a multiscale communication disorder in which immune, glial, vascular and systemic networks collectively shape tumour-permissive ecosystems. We discuss how network-based analyses identify communication hubs that can be translated into therapeutic hypotheses and provide a rationale to guide drug repurposing and rational combination strategies targeting tumour-microenvironment interactions. We further examine how multiscale modelling and artificial-intelligence-enabled integration of spatial and molecular data may enable patient stratification, response prediction, and adaptive trial designs, while highlighting key barriers, including data harmonization, spatial resolution limits, model interpretability and clinical deployment constraints. Reframing brain metastasis as a multiscale communication disorder provides a unifying conceptual and methodological foundation for therapies that disrupt metastatic ecosystems and improve durable intracranial disease control.

Lysinuric protein intolerance (LPI) is a rare disorder of dibasic amino acid transport associated with secondary urea cycle defects and immune dysregulation. Pregnancy in LPI is seldom reported and presents significant management challenges. We report a 25-year-old woman with genetically confirmed LPI complicated by prior hemophagocytic lymphohistiocytosis (HLH) and systemic lupus erythematosus (SLE) who presented with an unplanned pregnancy. Early gestation was characterised by metabolic instability, including transient hyperammonaemia and elevated orotic acid, which improved with optimisation of nitrogen scavenger therapy and nutritional support. Progressive severe thrombocytopenia and anaemia developed during the second trimester and were managed as presumed immune thrombocytopenia with corticosteroids and intravenous immunoglobulin. Delivery by caesarean section at 35 weeks resulted in favourable maternal and neonatal outcomes. To our knowledge, this represents the first reported case of pregnancy in LPI complicated by established immune dysregulation (HLH and SLE) and severe thrombocytopenia, defining a previously uncharacterised high-risk phenotype with a favourable outcome.

As the most extreme course of an infectious disease, sepsis poses a serious health threat, with a high mortality rate and frequent long-term consequences for survivors. Despite its enormous burden on global healthcare and ongoing research efforts, early sepsis onset prediction remains challenging due to the complex nature of its pathophysiology. Current approaches face a fundamental trade-off: data-driven machine learning models achieve strong performance but lack interpretability, while biologically inspired models provide mechanistic insights but have limited clinical validation. In this study, we propose the Latent Dynamics Model, a hybrid machine learning approach that integrates a functional model of coupled oscillators representing organ- and immune-cell populations and their interactions. Here, the model parameters encode physiological conditions and allow for an interpretable differentiation between healthy and pathological states. By projecting high-dimensional patient data into the low-dimensional parameter space of the functional model, machine-learned trajectories through this space allow the prediction of critical organ system states and simultaneously offer interpretability beyond plain risk estimates. The proposed method is trained and evaluated on real intensive care patients, achieving competitive AUROC/AUPRC performance on a retrospective MIMIC-IV cohort. Additional qualitative analysis reveals that the learned trajectories exhibit clinically plausible patterns of deterioration, recovery, and stability. We demonstrate that a physiological network model can be embedded within a deep learning architecture without compromising predictive performance while providing an interpretable latent structure for sepsis onset prediction.

Parkinson's disease (PD) imposes a growing socioeconomic burden due to its increasing prevalence and lack of a cure. Existing treatment options primarily manage motor and nonmotor symptoms but do not halt or slow disease progression, underscoring the urgent need for more effective and preventative strategies. Growing evidence suggests a strong link between immune system dysfunction, chronic inflammation, and the early pathogenesis of Parkinson's disease, often occurring years before the onset of motor symptoms, thereby indicating a critical window for early intervention. In this review, we examine current evidence on non-pharmacological approaches such as dietary changes, physical activity, and gut microbiome regulation, focusing on their potential to modulate both peripheral and central immune responses, thereby influencing the progression of PD. Besides being complementary to standard pharmacological treatments, these approaches not only reduce systemic inflammation but may also help delay, prevent, or improve clinical management of PD by targeting and modulating its immunological foundations.

High-throughput sequencing of B and T cell repertoires provides unprecedented insights into adaptive immunity but generates high-dimensional feature sets that are challenging to interpret. Standard dimensionality reduction techniques are often suboptimal for adaptive immune receptor repertoire (AIRR) data, which exhibits multi-collinearity, heterogeneous data types, and missingness. Here, we present VDJ-REMIX, an R package implementing a robust, network-based framework to deconstruct complex repertoire feature matrices into biologically interpretable modules. By refactoring weighted correlation network analysis (WGCNA), VDJ-REMIX provides a tailored workflow for preprocessing, imputation, and modularization of immune repertoire data. We demonstrate its utility across diverse contexts, including autoimmunity, inflammation, and acute infection. In autoimmune patients, VDJ-REMIX identified distinct B cell signatures that stratified diseases and revealed opposing dynamic responses to B cell-depleting versus anti-proliferative therapies. In COVID-19 and non-COVID-19 sepsis patients, it distinguished disease-specific signatures from shared severe infection responses and identified modules correlated with severity. Analysis of flow-sorted B cell populations stratified by FCGR2B genotype recapitulated known tolerance defects and uncovered population-specific repertoire signatures linked to inhibitory receptor dysfunction, providing orthogonal validation of module biological coherence. Finally, applied to a single-cell multi-omics dataset of immune cells in pancreatic ductal adenocarcinoma (PDAC) combining gene expression with AIRR-seq, VDJ-REMIX recovered modules linking BCR isotype usage and clonality to cytotoxic, interferon-responsive, and regulatory immune programmes. VDJ-REMIX is a versatile tool enabling systematic exploration of immunological variation and biomarker discovery from complex immune repertoire data. VDJ-REMIX is freely available at https://github.com/Bashford-Rogers-lab/vdjremix.

Cistanche deserticola Y.C.Ma is a well-known traditional medicinal herb widely used in traditional Chinese medicine for the treatment of kidney injury-related conditions, fatigue, infertility, and age-related disorders, as well as for improving immune function, and is traditionally prescribed for conditions associated with weakness and chronic inflammatory states. To investigate the potential efficacy of Tubuloside A (TA), an active constituent of Cistanche deserticola Y.C.Ma, against sepsis-induced splenic injury, a sepsis-associated structural and functional impairment of the spleen characterized by disrupted splenic architecture, dysregulated immune-cell homeostasis, excessive inflammatory responses, and weakened host defense, and to clarify its underlying mechanism of action. A murine cecal ligation and puncture (CLP) model and lipopolysaccharide (LPS)-stimulated J774A.1 macrophages were used to investigate the protective effects of TA against sepsis-induced splenic injury. Oxidative stress, antioxidant capacity, mitochondrial function, inflammatory responses, and apoptosis-related injury, splenic immune-cell composition, macrophage inflammatory phenotype, and F4/80/NOX4 colocalization were evaluated by biochemical assays, JC-1 staining, qPCR, Western blotting, flow cytometry, double immunofluorescence staining, and immunohistochemical analyses. Bone marrow-derived macrophages (BMDMs) were further used for supportive validation of macrophage-related inflammatory responses and NOX4 expression. Integrative network pharmacology and molecular docking were employed to identify candidate molecules potentially associated with TA-mediated protection, and NADPH oxidase 4 (NOX4) overexpression was used to further examine the involvement of NOX4-associated oxidative stress in vitro. TA significantly alleviated splenic injury and improved survival in septic mice. TA reduced oxidative stress, as evidenced by decreased malondialdehyde and reactive oxygen species (ROS) levels and enhanced antioxidant defenses, including superoxide dismutase, catalase, glutathione, and total antioxidant capacity. TA restored mitochondrial membrane potential and improved mitochondrial homeostasis, accompanied by increased TOM20, GPX4, and PGC-1α expression and reduced Drp1 expression. In addition, TA suppressed pro-inflammatory mediators, including TNF-α, IL-1β, IL-6, and iNOS, increased anti-inflammatory IL-10 expression, and reduced Bax and cleaved caspase-3/9 levels, indicating inhibition of apoptosis-related injury. Flow cytometry and BMDM validation further showed that TA regulated splenic immune-cell alterations and macrophage inflammatory responses, while F4/80/NOX4 double immunofluorescence staining indicated that NOX4 expression was associated with F4/80-positive macrophages in splenic tissues. Mechanistically, network pharmacology and molecular docking suggested that NOX4-associated oxidative stress may be involved in TA-mediated protection, which was further supported by the marked induction of NOX4 during sepsis and by the finding that NOX4 overexpression significantly blunted the protective effects of TA in vitro. This study demonstrates that TA exerts a protective effect against sepsis-induced splenic injury by suppressing NOX4-associated oxidative stress, preserving mitochondrial homeostasis, and limiting downstream inflammatory and apoptotic damage. These findings not only expand the pharmacological profile of TA, but also provide experimental support for the therapeutic potential of an active constituent from Cistanche deserticola Y.C.Ma in sepsis-related immune-organ injury, particularly through the regulation of oxidative stress, macrophage-associated inflammatory responses, and splenic immune-cell alterations.

In recent years, the role of gut microbiota (GM) in the pathogenesis of allergic diseases (ADs) has garnered increasing attention, yet the influence of the brain and its metabolites (such as cerebrospinal fluid [CSF] metabolites) remains unclear. This study aims to investigate whether GM mediates its effect on allergic rhinitis (AR) through CSF metabolites and to quantify this mediating effect, while systematically evaluating the causal relationships between brain functional networks/CSF metabolites and multiple ADs to elucidate the potential roles of brain-related factors in allergic mechanisms. Using large-scale genome-wide association study data, including GM (n = 7738), brain functional networks (n = 47,276), CSF metabolites (n = 291), and 7 categories of AD (Finnish R12 database), we applied Mendelian randomization (MR) for causal inference. Specifically, we performed: mediation MR to test the GM-CSF metabolites-AR pathway; and 2-sample MR to assess causal relationships between brain functional networks/CSF metabolites and ADs. Genetically predicted analyses revealed significant associations between GM and both CSF metabolites and AR. Mediation MR confirmed that Peptococcia abundance exerts a positive causal effect on AR (odds ratio [OR] = 1.55, 95% confidence interval [CI] = 1.23-1.95, P = .0002), which was partially mediated by CSF methyl succinoyl-carnitine levels, with a mediation proportion of 9.02%. Extended analyses indicated that CSF metabolites are positively associated with allergic contact dermatitis and pollen allergy: specifically, methyl succinoyl-carnitine levels correlated positively with pollen allergy, and indoleacetate levels with allergic contact dermatitis (both significant after false discovery rate correction). Brain functional network analyses showed allergic urticaria was negatively correlated with 9 functional networks, including the occipital-precuneus and postcentral gyrus. This study is the first, via MR, to demonstrate that GM may partially influence AR risk through CSF metabolites (e.g., methyl succinoyl-carnitine); concurrently, brain functional networks regulate specific ADs. These findings deepen understanding of the "gut-brain-immune" axis mechanism and provide new directions for future precise interventions targeting metabolites and neuroimmune pathways.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with limited non-invasive biomarkers and variable responses to probiotics. This study investigates the probiotic potential of Enterococcus hirae Y-HS isolated from healthy beef cattle and its mechanisms in alleviating UC. In vitro probiotic properties of Y-HS were assessed. Public transcriptomic datasets (GSE179285, GSE87466, GSE206285) were analysed to identify differentially expressed genes in UC patients. Machine learning integrated with protein-protein interaction network analysis identified core diagnostic genes. A DSS-induced murine colitis model was established to evaluate Y-HS intervention effects. Y-HS exhibited excellent gastrointestinal tolerance, no haemolytic activity and antibiotic susceptibility. Transcriptomic analysis identified 768 DEGs in UC patients. Machine learning yielded four metabolism-associated signature genes-CYP3A4, UGT1A6, HSD17B6 and SRD5A3-with diagnostic accuracy (AUC 0.72-0.84). In DSS-induced colitis, Y-HS dose-dependently attenuated disease activity, remodelled gut microbiota (increasing Lactobacillus, decreasing Escherichia-Shigella), activated PXR/Nrf2 signalling, upregulated detoxification enzymes (CYP3A4, UGT1A6) and tight junction proteins, while downregulating HSD17B6, SRD5A3 and cleaved caspase-3. These changes were accompanied by reduced pro-inflammatory cytokines and elevated IL-10. E. hirae Y-HS alleviates UC through coordinated modulation of gut microbiota, host metabolism, inflammation and barrier function. The identified metabolic gene signature offers potential non-invasive biomarkers for UC.