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Mapping the Immune cell-specific gene regulatory network in bipolar disorder: A framework from scTWMR to exploratory drug-target annotation.

BACKGROUND: Although the involvement of the immune system in the genetic susceptibility of bipolar disorder (BD) is widely acknowledged, the causal relationship between gene expression in specific immune cell subtypes and BD requires systematic elucidation. METHODS: We implemented an analytical framework integrating single-cell transcriptome-wide Mendelian randomization (scTWMR) with colocalization analysis. This approach utilized cis-expression quantitative trait loci (cis-eQTLs) derived from 14 distinct immune cell types as instrumental variables to interrogate BD genome-wide association study (GWAS) summary statistics (comprising 41,917 cases and 371,549 controls). Subsequent investigations encompassed functional enrichment analysis, protein-protein interaction (PPI) network construction, phenome-wide association study (PheWAS), and performed an exploratory drug-target annotation. RESULTS: Our analysis identified 33 gene-immune cell associations. Colocalization analysis provided robust evidence (PPH4 > 90%) for shared causal variants implicating the MAD1L1, APOM, and NFKBIL1 loci. Significantly enriched biological pathways included cell cycle regulation, circadian rhythm entrainment, and neuroinflammation. The PPI network revealed a core regulatory module centered on histone-encoding and immune-related genes. Exploratory drug-target annotation nominated compounds for further investigation for compounds targeting APOM, TMEM258, and NFKBIL1. CONCLUSION: This study systematically delineates a genetically supported regulatory network of immune cell-specific gene expression in BD, predominantly implicating CD8⁺ effector T cells, plasma cells, and B cells. The findings corroborate established pathological pathways while uncovering novel cell type-specific therapeutic targets, thereby providing a genetic framework for prioritizing candidate targets for future investigation.

Bipolar disorder↗

Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of &#x3b2; = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-&#x3ba;B, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 &#xb1; 0.83 kcal&#xb7;mol-&#xb9;) and HCAR3 with 3-Indolepropionic Acid (-6.35 &#xb1; 0.70 kcal&#xb7;mol-&#xb9;), both below -5.0 kcal&#xb7;mol-&#xb9;. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.

Molecular Docking Simulation↗

Quantitative Proteomic Analysis of APP/PS1 Transgenic Mice.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the central nervous system (CNS), with its etiology still shrouded in uncertainty. The interplay of extracellular amyloid-&#x3b2; (A&#x3b2;) deposition, intracellular neurofibrillary tangles (NFTs) composed of tau protein, cholinergic neuronal impairment, and other pathogenic factors is implicated in the progression of AD. OBJECTIVE: The current study endeavors to delineate the proteomic landscape alterations in the hippocampus of an AD murine model, utilizing proteomic analysis to identify key physiological and pathological shifts induced by the disease. This endeavor aims to shed light on the underlying pathogenic mechanisms, which could facilitate early diagnosis and pave the way for novel therapeutic interventions for AD. METHODS: To dissect the proteomic perturbations induced by A&#x3b2; and Presenilin-1 (PS1) in the AD pathogenesis, we undertook a label-free quantitative (LFQ) proteomic analysis focusing on the hippocampal proteome of the APP/PS1 transgenic mouse model. Employing a multi-faceted approach that included differential protein functional enrichment, cluster analysis, and protein-protein interaction (PPI) network analysis, we conducted a comprehensive comparative proteomic study between APP/PS1 transgenic mice and their wild-type C57BL/6 counterparts. RESULTS: Mass spectrometry identified a total of 4817 proteins in the samples, with 2762 proteins being quantifiable. Comparative analysis revealed 396 proteins with differential expression between the APP/PS1 and control groups. Notably, 35 proteins exhibited consistent temporal regulation trends in the hippocampus, with concomitant alterations in biological pathways and PPI networks. CONCLUSIONS: This study presents a comparative proteomic profile of transgenic (APP/PS1) and wild-type mice, highlighting the proteomic divergences. Furthermore, it charts the trajectory of proteomic changes in the AD mouse model across the developmental stages from 2 to 12 months, providing insights into the physiological and pathological implications of the disease-associated genetic mutations.

Animals↗

MicroRNA-486: a dual-function biomarker for diagnosis and tumor immune microenvironment characterization in non-small cell lung cancer.

BACKGROUND: This investigation evaluates the clinical significance and molecular mechanisms of microRNA-486 (miR-486) as a potential biomarker in non-small cell lung cancer (NSCLC) through an integrative analytical approach. METHODS: We conducted systematic search and meta-analysis of diagnostic studies from major biomedical databases from inception through April 04, 2025, followed by comprehensive bioinformatics interrogation. Protein-protein interaction (PPI) networks were constructed using STRING to identify key hub genes regulated by miR-486. Validation of hub genes employed TCGA datasets, while immune infiltration analysis utilized TIMER2.0 platform. RESULTS: The meta-analysis indicated that miR-486, both individually and in combination, could be effective biomarkers for NSCLC detection. Afterwards, functional enrichment analyses of miR-486 target genes highlighted significant ontology terms and pathways crucial to the initiation and progression of NSCLC. PPI networks revealed key proteins and modules that participate in multiple essential pathways associated with NSCLC pathogenesis. Furthermore, the identified hub genes were validated for differential expression in cancerous versus normal tissues, suggesting their potential diagnostic utility, while subsequent survival analyses confirmed their prognostic value through significant associations with overall survival. Notably, these hub genes were found to be significantly associated with immune infiltration levels, immune microenvironment scores, and immune-related proteins in NSCLC. CONCLUSIONS: This dual-modality investigation establishes miR-486 as a multi-functional biomarker in NSCLC, demonstrating both diagnostic utility and immunoregulatory potential through tumor microenvironment modulation.

Humans↗

Integrated analysis of amide proton transfer weighted MRI and proteomics uncovers altered protein dynamics in glioblastoma.

PURPOSE: Elevated amide proton transfer-weighted (APTw) MRI signals in glioblastoma (GBM) are often linked to increased intracellular mobile proteins, but the associated molecular patterns in human tissue remain unclear. We examined the relationship between regional APTw features and cellular protein composition and profiled proteomic differences between tumor and peritumoral tissue. METHODS: In this single-center prospective study, preoperative MRI data were integrated with intraoperative neuronavigation for 12 image-guided tissue samples (8 tumor and 4 peritumoral). Total, cytoplasmic, and nuclear proteins were quantified using bicinchoninic acid (BCA) assay. Data-independent acquisition (DIA) proteomics identified exploratory differentially expressed proteins (DEPs), followed by functional enrichment and protein-protein interaction (PPI) network analyses. Transcript-level expression patterns and survival associations were queried in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets to provide indirect external clinical context. RESULTS: Tumor regions showed higher APTw signals than peritumoral regions (p&#x2009;<&#x2009;0.001) and increased cytoplasmic protein concentration (p&#x2009;<&#x2009;0.05), without a corresponding increase in total or nuclear protein levels. DIA identified 654 DEPs. Further analysis highlighted 36 higher-significance DEPs, and prioritized 12 hub proteins in the PPI network. In public transcriptomic datasets, ERBB2, RUNX1, and SHC1 showed higher expression in GBM and were associated with poorer overall survival. CONCLUSION: These findings suggest that elevated APTw signal in GBM may be associated with increased cytoplasmic protein content and distinct proteomic alterations. This imaging-proteomic framework provides exploratory regional context for future mechanistic and follow-up studies, but larger, spatially matched and independently validated cohorts are required to confirm the molecular contributors to APTw contrast.

Humans↗

Identification and validation of the important role of KIF11 in the development and progression of endometrial cancer.

BACKGROUND: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies. METHODS: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC. Functional enrichment analysis of the DEGs was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. DEGs were further screened for hub genes through protein-protein interaction (PPI) network analysis and machine learning. The role of the hub gene KIF11 in EC was analyzed using clinical data from the TCGA database. The expression of KIF11 in EC was subsequently validated in clinical samples. In vitro experiments were utilized to evaluate the effects of KIF11 on biological functions such as proliferation, migration, apoptosis, and the cell cycle in endometrial cancer cells. RESULTS: A total of 877 DEGs, which are widely involved in important biological processes such as cell division, tubulin binding, and the cell cycle, were identified. Through PPI network analysis and machine learning, KIF11 was selected as the hub gene for subsequent analysis and experimental validation. An analysis of TCGA data revealed that KIF11 is highly expressed in EC and is associated with tumor grade, stage, and a low survival rate. The overexpression of KIF11 in tumor tissues was further confirmed in EC patient samples. KIF11 knockdown had inhibitory effects on cell proliferation, migration and invasion. Flow cytometry analysis revealed that KIF11 knockdown induced G2/M phase arrest and promoted apoptosis in EC cells. CONCLUSION: Our study demonstrated that KIF11 was upregulated in EC and was strongly associated with a poor prognosis. Notably, we found that reduced KIF11 expression inhibited EC cell proliferation, migration and invasion. KIF11 knockdown caused more EC cells to arrest in the G2/M phase and undergo apoptosis. The findings of our study emphasized that KIF11 may be a promising prognostic biomarker and therapeutic target for EC patients.

Humans↗

Deciphering the molecular nexus of BTG2 in periodontitis and diabetic kidney disease.

OBJECTIVE: To investigate the role of BTG2 in periodontitis and diabetic kidney disease (DKD) and its potential underlying mechanism. METHODS: Gene expression data for periodontitis and DKD were acquired from the Gene Expression Omnibus (GEO) database. Differential expression analysis identified co-expressed genes between these conditions. The Nephroseq V5 online nephropathy database validated the role of these genes in DKD. Pearson correlation analysis identified genes associated with our target gene. We employed Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) networks to elucidate potential mechanisms. Expression levels of BTG2 mRNA were examined using quantitative polymerase Chain Reaction (qPCR) and immunofluorescence assays. Western blotting quantified proteins involved in epithelial-to-mesenchymal transition (EMT), apoptosis, mTORC1 signaling, and autophagy. Additionally, wound healing and flow cytometric apoptosis assays evaluated podocyte migration and apoptosis, respectively. RESULTS: Analysis of GEO database data revealed BTG2 as a commonly differentially expressed gene in both DKD and periodontitis. BTG2 expression was reduced in DKD compared to normal conditions and correlated with proteinuria. GSEA indicated enrichment of BTG2 in the EMT and mTORC1 signaling pathways. The PPI network highlighted BTG2's relevance to S100A9, S100A12, and FPR1. Immunofluorescence assays demonstrated significantly lower BTG2 expression in podocytes under high glucose (HG) conditions. Reduced BTG2 expression in HG-treated podocytes led to increased levels of EMT markers (&#x3b1;-SMA, vimentin) and the apoptotic protein Bim, alongside a decrease in nephrin. Lower BTG2 levels were associated with increased podocyte mobility and apoptosis, as well as elevated RPS6KB1 and mTOR levels, but reduced autophagy marker LC3. CONCLUSION: Our findings suggest that BTG2 is a crucial intermediary gene linking DKD and periodontitis. Modulating autophagy via inhibition of the mTORC1 signaling pathway, and consequently suppressing EMT, may be pivotal in the interplay between periodontitis and DKD.

Periodontitis↗

Transcriptomic Changes Associated with Electroacupuncture in a DMCAO Model of Delayed Cognitive Impairment.

INTRODUCTION: Delayed Cognitive Impairment (DCIS) occurs in approximately 31% to 77% of individuals following stroke. Clinical findings have indicated that electroacupuncture may alleviate post-stroke DCIS. However, insights derived from animal models remain limited. The present study utilized a Distal Middle Cerebral Artery Occlusion (DMCAO) mouse model to investigate the potential mechanisms of electroacupuncture through hippocampal transcriptomic analysis. MATERIALS AND METHODS: Adult male BALB/c mice were subjected to DMCAO and received electroacupuncture treatment. High-throughput RNA sequencing of hippocampal tissue was performed to identify Differentially Expressed Genes (DEGs). Enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, hierarchical clustering, and Protein-Protein Interaction (PPI) network analysis, were performed to elucidate potential biological mechanisms. RESULTS: The DMCAO model exhibited features consistent with DCIS. Electroacupuncture treatment was associated with improved cognitive performance and enhanced hippocampal neuroplasticity. A total of 116 DEGs were identified in the DMCAO group compared with the sham group, while 69 DEGs were identified in the DMCAO + electroacupuncture group compared with the untreated DMCAO group. DISCUSSION: GO enrichment analysis indicated that electroacupuncture modulated biological processes related to nerve fibers, axonal development, neuronal regulation, cellular processes, and cardiovascular protection. KEGG pathway analysis indicated involvement in pathways associated with neuronal recovery and axonal function. The PPI network comprised 28 nodes and 33 interactions, with hub genes such as Gna13, Hipk2, and Stambp playing key roles. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) results were consistent with RNA sequencing findings. CONCLUSION: Electroacupuncture improved DCIS in the DMCAO mouse model. Transcriptomic analysis of the hippocampus provided preliminary evidence of the potential mechanisms underlying the therapeutic effects of electroacupuncture treatment following ischemic stroke.

Animals↗

Bioinformatics Analysis and Experimental Validation of Key Genes Associated With Hypoxia and Ischemia in Myocardial Infarction.

BACKGROUND: This study aimed to screen and identify core hypoxia-ischemia-related genes associated with myocardial infarction (MI). METHOD: Two transcriptomic datasets, GSE97320 and GSE48060, were retrieved from the Gene Expression Omnibus (GEO) database. After data integration and batch effect elimination, differential expression analysis was performed to screen differentially expressed genes (DEGs), and the corresponding visualization analysis was conducted. Hypoxia-ischemia-related genes were acquired from the GeneCards database; hypoxia-ischemia related genes (HIRGs) were subsequently identified by intersecting the retrieved genes with screened DEGs. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented to explore the biological functions and underlying signaling pathways of HIRGs. A combination of protein-protein interaction (PPI) network analysis and random forest (RF) algorithm was applied to screen hub genes from HIRGs. The external GEO dataset GSE66360 was utilized to validate the expression patterns of candidate hub genes. Furthermore, an acute myocardial infarction (AMI) mouse model was established, and quantitative real-time polymerase chain reaction (qPCR) was performed to detect the mRNA expression levels of hub genes in myocardial tissues for in&#xa0;vivo validation. RESULTS: A total of 633 DEGs and 308 hypoxia-ischemia-related genes were screened in the present study, among which 21 overlapping HIRGs were obtained. PLAUR and IL1B were finally identified as two hub genes from HIRGs based on PPI network and random forest algorithm. The qPCR results revealed that the expression levels of PLAUR and IL1B were significantly upregulated in the AMI group compared with the sham operation group (p&#x2009;<&#x2009;0.05). CONCLUSION: The present findings demonstrated that PLAUR and IL1B serve as pivotal genes involved in the pathological hypoxia-ischemia process of AMI. These two genes may act as novel biomarkers and promising therapeutic targets for the recognition and clinical intervention of hypoxia-ischemia injury following AMI.

Myocardial Infarction↗

Protein complex prediction via cost-based clustering.

MOTIVATION: Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein-protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach to protein complex identification. RESULTS: We have developed the Restricted Neighborhood Search Clustering Algorithm (RNSC) to efficiently partition networks into clusters using a cost function. We applied this cost-based clustering algorithm to PPI networks of Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans to identify and predict protein complexes. We have determined functional and graph-theoretic properties of true protein complexes from the MIPS database. Based on these properties, we defined filters to distinguish between identified network clusters and true protein complexes. CONCLUSIONS: Our application of the cost-based clustering algorithm provides an accurate and scalable method of detecting and predicting protein complexes within a PPI network.

Algorithms↗

Mechanism of Qigu capsule as a treatment for sarcopenia based on network pharmacology and experimental validation.

OBJECTIVE: To explore the potential molecular mechanism of Qigu capsule (&#xff0c;QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally. METHODS: The active compounds of QGC and common targets between QGC and sarcopenia were screened from databases. Then the herbs-compounds-targets network, and protein-protein interaction (PPI) network was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by R software. Next, we used a dexamethasone-induced sarcopenia mouse model to evaluate the anti-sarcopenic mechanism of QGC. RESULTS: A total of 57 common targets of QGC and sarcopenia were obtained. Based on the enrichment analysis of GO and KEGG, we took the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway as a key target to explore the mechanism of QGC on sarcopenia. Animal experiments showed that QGC could increase muscle strength and inhibit muscle fiber atrophy. In the model group, the expression of muscle ring finger-1 and Atrogin-1 were increased, while myosin heavy chain was decreased, QGC treatment reversed these changes. Moreover, compared with the model group, the expressions of p-PI3K, p-Akt, p-mammalian target of rapamycin and p-Forkhead box O3 in the QGC group were all upregulated. CONCLUSION: QGC exerts an anti-sarcopenic effect by activating PI3K/Akt signaling pathway to regulate skeletal muscle protein metabolism.

Sarcopenia↗

Mechanism of action of curculigoside ameliorating osteoporosis: an analysis based on network pharmacology and experimental validation.

OBJECTIVE: This study aimed to predict and verify the mechanism of curculigoside in treating osteoporosis using network pharmacology, molecular docking technology, and micro-CT technology. METHODS: Herb databases were searched to identify and screen potential targets of curculigoside. The GeneCards platform was utilized to mine osteoporosis-related targets. Cytoscape 3.6.0 software was employed to construct a compound-target-disease network. A protein-protein interaction (PPI) network for curculigoside in osteoporosis treatment was established, and core targets were screened. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and GO biological process analyses were performed using the Metascape database. Finally, molecular docking and micro-CT were used to validate core targets relevant to osteoporosis. RESULTS: A total of 166 potential curculigoside targets and 4,313 osteoporosis-related targets were identified, with 91 common targets. Ten key targets, including matrix metalloproteinase (MMP)3, MMP9, interleukin (IL)-6, and caspase-3, were screened. KEGG pathway enrichment analysis indicated involvement in 10 pathways, such as the Rap1 signaling pathway and tumor necrosis factor (TNF) signaling pathway. Molecular docking results demonstrated strong binding affinity between curculigoside and the core targets. Micro-CT analysis revealed that curculigoside not only improved BMD, BV/TV, BS/BV, and Tb.Th but also reduced Tb.Sp in osteoporotic bone. CONCLUSIONS: Curculigoside is likely to treat osteoporosis through targets such as MMP3, MMP9, IL-6, and caspase-3, acting on signaling pathways including Rap1 and TNF. These results indicate that curculigoside exhibits multitarget and multipathway characteristics in osteoporosis treatment, providing a theoretical basis for further clinical investigation.

Osteoporosis↗

Integrated Network Pharmacology and Molecular Docking Analysis of Sishen Decoction Identifies Potential Targets and Pathways in Gout.

Gout is a disease characterized by hyperuricemia and the deposition of urate crystals in joints and soft tissues, leading to recurrent acute arthritis. Its increasing prevalence imposes substantial clinical and socioeconomic burdens. Sishen Decoction (SSD) has been used in the treatment of gout, but its potential molecular mechanisms remain unclear. This study applied an integrated network pharmacology and molecular docking approach to identify potential targets and signaling pathways associated with SSD in gout. Active compounds and corresponding targets of SSD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), while gout-related targets were collected from the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Overlapping targets were identified and used to construct a drug-component-target-disease network. A protein-protein interaction (PPI) network was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed, followed by molecular docking using the docking server analysis module. A total of 37 bioactive compounds were associated with 116 overlapping gout-related targets. The top hub targets included TP53, IL6, IL1B, TNF, AKT1, EGFR, CASP3, JUN, BCL2, and MMP9. GO analysis suggested that these targets are involved in gene expression regulation and signal transduction. KEGG enrichment analysis indicated significant associations with the mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase/protein kinase B (PI3K-Akt), interleukin-17 (IL-17), and tumor necrosis factor (TNF) signaling pathways. Molecular docking predicted favorable interactions between key compounds and hub targets, with all binding energies of &#x2264;-5 kcal/mol. These computational findings provide potential mechanistic hypotheses for the action of SSD in gout and may support future experimental validation.

Molecular Docking Simulation↗

Bioinformatics identification and validation of pyroptosis-related gene for ischemic stroke.

BACKGROUND: Ischemic stroke (IS) is one of the common and frequent diseases with extremely high lethality and disability in the world, and there is no effective treatment at present. This study aimed to screen hub genes involved in cerebral ischemia/reperfusion injury (CIRI) and pyroptosis, and explore promising intervention targets. METHODS: CIRI-related genes (GSE202659 and GSE131193) and pyroptosis-related genes (PRGs) in mice were obtained from the Gene Expression Omnibus (GEO) and GeneCards database. We screened for LASSO regression to construct a prognostic model of GSE131193 and PRGs and examined by GSE137482. The functional enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on pyroptosis-related differentially expressed genes (PRDEGs) of GSE202659.The key modules for CIRI and pyroptosis were identified by Weight Gene Co-expression Network Analysis (WGCNA). Subsequently, Protein-protein Interaction (PPI) network and the Cytoscape was constructed to screen out hub genes. Used the starBase to predict miRNA interacting with hub genes and constructed mRNA-miRNA-lncRNA interaction networks. CIRI-related Molecular Subtypes were constructed for hub genes. The relationship between immune cells and hub genes was verified via CIBERSORT. Finally, we selected C57BL/6 mice to construct models to confirm hub genes by enzyme linked immunosorbent assay (ELISA), reverse transcription-polymerase chain reaction (RT-PCR), western blot, and Immunofluorescence. RESULTS: A total of 272 PRGs and 35 PRDEGs were screened. An eight-gene risk prediction models were established (AUC&#x2009;=&#x2009;0.868). GO, KEGG, GSEA and GSVA analyses revealed that PRDEGs were mainly involved in positive regulation of cytokine production, and NOD-like receptor signaling pathway. And then, seven hub genes (Irf1, Icam1, Tlr2, Tnf, Cebpb, Il1rn, and Casp8) were identified by PPI. Icam1, Tnf, Cebpb, Il1rn, and Casp8 had high expression profiles in Cluster2 by hierarchical clustering. The immune infiltration analysis results showed that among the hub genes, Cebpb, Il1rn, and Casp8, showed a significant positive correlation with the degree of NK.Actived, and Icam1 showed a significant negative correlation with B.Cells.Memory. The results of animal experiments significantly demonstrated an upregulation of Irf1, Icam1, Tlr2, Cebpb, and Il1rn. CONCLUSION: Our finding indicated that Irf1, Icam1, Tlr2, Cebpb, and Il1rn are hub genes associated with pyroptosis, and these genes are all associated with different immune cells, so as to provide new targets for the prevention and treatment of IS from the perspective of pyroptosis.

Pyroptosis↗

Stage-dependent proteomic alterations in aqueous humor of diabetic retinopathy patients based on data-independent acquisition and parallel reaction monitoring.

BACKGROUND: Diabetic retinopathy (DR), a microvascular complication of diabetes mellitus (DM), represents the predominant cause of preventable vision loss in working-age populations globally. While the pathophysiological mechanisms underlying DR progression remain incompletely understood, our study employs comprehensive proteomic profiling of aqueous humor (AH) to identify stage-specific biomarkers and therapeutic targets in type 2 diabetes mellitus (T2DM) patients across DR progression. METHODS: Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified AH proteomes in a discovery cohort comprising 24 subjects: 18 T2DM patients stratified by DR severity [6 non-DR, 6 non-proliferative DR (NPDR), 6 proliferative DR (PDR)] and 6 cataract controls without diabetes (non-DM). Validation cohort analysis (including 10 AH samples in each group) was performed using parallel reaction monitoring (PRM) strategy for verification of target proteins. Comprehensive bioinformatics analyses included gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, receiver operating characteristic (ROC) curve analysis, and ConnectivityMap (Cmap)-based drug prediction. RESULTS: Proteomic profiling identified 739 quantifiable AH proteins (62% extracellular) with clear separation among the four clinical stages in the discovery cohort. GSEA uncovered altered expression of proteins mainly related to complement and coagulation cascades, folate metabolism, and the selenium micronutrient network in patients with DR. WGCNA-derived protein modules yielded 83 PRM-validated targets, including 5 hub proteins differentiating NPDR from non-DR and 33 hub proteins showed significant upregulation in PDR versus NPDR comparison. Clinical correlation analysis identified F2, FGG, FGB, RBP4, AMBP, VTN, C8A, CPB2, and C2 associated with clinical traits. C6, FAM3C, SPP1, and JCHAIN levels were altered post-anti-VEGF treatment. Pharmacological prediction identified potential therapeutic compounds, including perindopril, triciribine, and XAV-939 for NPDR, and topiramate, triciribine, and vecuronium for PDR. CONCLUSION: This study established a comprehensive AH proteomic signature of DR progression, offering insights into the pathogenesis of DR and highlighting potential biomarkers and novel therapeutic targets.

Humans↗

Systematic understanding of mechanism of Shenfu decoction improve the prognosis of ischemic stroke using a network pharmacology and animal experiment approach.

OBJECTIVE: To explore the active compounds and the mechanism of Shenfu decoction (, SFD) against ischemic stroke (IS) through network pharmacology and animal experiments. METHODS: SFD components were retrieved from the Traditional Chinese Medicine (TCM) database. The Online Mendelian Inheritance in Man (OMIM), Comparative Toxicogenomics Database (CTD) and Therapeutic Target Database (TTD) database were used to retrieve the IS-related disease targets. The herb-compound-target network was built by Cytoscape 3.7.1 software. The core targets were obtained using protein-protein interaction (PPI) network. The core targets of SFD were further analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We then performed molecular docking between the hub proteins and key active compounds. Finally, we conducted animal experiments to verify the regulation of SFD on apoptosis following IS. RESULTS: There were 221 corresponding targets and 25 components related to Chinese medicine throughout the compound-target network. The core targets of SFD in the treatment of IS was tumor protein P53 (Tp53), mitogen-activated protein kinase 3 (MAPK3), MAPK1, heat shock proteins 90AA1 and alpha serine/threonine-protein kinase1. There were 221 GO items in GO function enrichment analysis and 106 signaling pathways in KEGG, mainly including negative regulation of the apoptosis process, vascular endothelial growth factor signaling pathways, NOD-like receptor signaling pathway, etc. Among them, Tp53, MAPK3, and MAPK1 were docked with small molecule compounds. Through animal research, we confirmed the effect of SFD on apoptosis following stroke. CONCLUSION: This study demonstrates that SFD can treat IS through multiple targets and pathways, and provides new perspectives for exploring the core targets and mechanisms of SFD against IS.

Drugs, Chinese Herbal↗

[Effects and mechanisms of Jiawei Yigong San on the Th17/Treg balance in food allergy].

Objective To explore the effects and mechanisms of Jiawei Yigong San (JWYGS) on the T helper type 17 (Th17)/regulatory T (Treg) cell balance in food allergy (FA). Methods Active components, action targets of JWYGS, and FA-related targets, were screened via network pharmacology. Overlapping targets between JWYGS and FA were used to construct a protein-protein interaction (PPI) network. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed to predict key signaling pathways. Molecular docking was conducted to validate the binding affinity between the main active components and the predicted targets. Mice were randomly divided into control group, model group, JWYGS low-dose, medium-dose, and high-dose groups, and dexamethasone (DXM) group. An ovalbumin (OVA)-induced FA model was established. During the OVA challenge period, mice received daily intragastric administration, after which allergy and diarrhea scores were assessed. Small intestinal pathology was evaluated by HE staining. Serum ovalbumin-specific immunoglobulin E (OVA-sIgE), interleukin 6 (IL-6), IL-17, IL-2, and IL-10 were measured by ELISA. Small intestinal IL-6, IL-17, and IL-10 protein expression was detected by immunohistochemistry. Splenic Th17 and Treg cells were quantified by flow cytometry, and the Th17/Treg ratio was calculated. The mRNA expression of IL-6, retinoic acid receptor-related orphan receptor &#x3b3;t (ROR&#x3b3;t), and forkhead box protein P3 (FOXP3) in the small intestine was detected by qPCR. Results Network pharmacology identified 235 active components of JWYGS and 136 common targets. GO and KEGG enrichment analyses highlighted cytokine response and Th17 differentiation. Molecular docking confirmed stable binding between core components and targets. Compared with the control group, the model group exhibited aggravated allergy and diarrhea scores, marked small intestinal inflammation and mucosal damage, elevated serum levels of OVA-sIgE, IL-6, IL-17 and IL-2, along with increased splenic Th17 cell frequency and Th17/Treg ratio. Intestinal IL-6 and IL-17 protein levels as well as IL-6 and ROR&#x3b3;t mRNA expression were upregulated, whereas serum IL-10 levels were decreased, and intestinal expression of IL-10 protein and FOXP3 mRNA was downregulated. After JWYGS treatment, allergy and diarrhea scores were significantly reduced. Small intestinal inflammation and mucosal damage were alleviated. Serum levels of OVA-sIgE, IL-17, IL-6 and IL-2, Th17 cell frequency and the Th17/Treg ratio, intestinal IL-6 and IL-17 protein levels were decreased. IL-6 and ROR&#x3b3;t mRNA expression was downregulated. Serum IL-10 levels were increased and intestinal expression of IL-10 protein and FOXP3 mRNA was upregulated. Moreover, the JWYGS high-dose group demonstrated comparable efficacy to the DXM group. Conclusion JWYGS can ameliorate symptoms and reverse the Th17/Treg imbalance in FA mice, potentially by inhibiting IL-6 transcription and regulating ROR&#x3b3;t/FOXP3 expression.

Animals↗

Acupoint Selection Patterns and Potential Mechanisms of Acupuncture in Knee Osteoarthritis: A Combined Data Mining and Network Pharmacology Study.

OBJECTIVE: To identify the core acupoint prescription and Kellgren-Lawrence (K-L) grade-dependent compatibility patterns of acupuncture for KOA through complex network analysis, and to predict the potential molecular mechanisms underlying the core prescription via network pharmacology. METHODS: Literature was retrieved from PubMed, EMbase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and SinoMed (inception to September 3, 2025). Frequency, association rule, complex network, and K-L grade subgroup analyses were applied. Potential targets of the core prescription were identified via network pharmacology and intersected with disease targets from OMIM, Therapeutic Target, GeneCards, and DrugBank. A protein-protein interaction (PPI) network was constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential molecular mechanisms. RESULTS: We included 522 studies, yielding 582 prescriptions involving 123 acupoints. The core prescription comprised 24 acupoints, including Dubi (ST35), Neixiyan (EX-LE4), Liangqiu (ST34), Xuehai (SP10), Zusanli (ST36), Yanglingquan (GB34), Yinlingquan (SP9), among others. K-L subgroup analysis revealed ST35, GB34, SP9, and SP10 as universal core acupoints. The mild-to-moderate subgroup mainly used local acupoints, while the moderate-to-severe subgroup centered on ST35, with increased distal acupoint usage and higher degree values. Network pharmacology analysis identified 77 overlapping targets. Core targets included tumor necrosis factor (TNF), interleukin 6 (IL6), interleukin 1 beta (IL1B), tumor protein p53 (TP53), matrix metallopeptidase 9 (MMP9), signal transducer and activator of transcription 3 (STAT3), transforming growth factor beta 1 (TGFB1), caspase 3 (CASP3), and B-cell lymphoma 2 (BCL2), which were enriched in inflammation and immunity, cartilage metabolism, and tissue repair pathways. CONCLUSION: The core acupoint prescription for KOA features local acupoints combined with distal ones, exhibiting distinct patterns across K-L grades. Our computational findings suggest that core acupoints may potentially delay knee joint degeneration by synergistically regulating inflammation, cartilage metabolism, apoptosis, and tissue repair, although these predictions require experimental validation. These findings provide preliminary evidence and a theoretical basis for standardized clinical point selection and further mechanistic research.

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