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PubMed2025

Construction and accuracy assessment of an efferocytosis-related prognostic model for ovarian cancer: A diagnostic accuracy study.

The study aimed to investigate the prognostic significance of efferocytosis-related genes in ovarian cancer (OC) with regard to cancer development, progression, invasion, and metastasis. OC cohorts were assembled from bioinformatics repositories. Utilizing consensus clustering analysis, distinct clusters were delineated based on the intersection of OC-related genes and efferocytosis-related genes. A prognostic signature specific to efferocytosis in OC was developed using data from The Cancer Genome Atlas, validated against the gene expression omnibus database, and subjected to independent prognostic analysis. Subsequently, a nomogram model was formulated. Moreover, investigations encompassed the immune microenvironment, immunotherapy, mutation profiling, drug sensitivity assessments, drug prediction models, and molecular docking analyses. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were employed to ascertain the mRNA expression levels of key genes. Five key genes, FCGBP, BTN3A3, WDR91, SLC25A45, and BTNL3, were identified as significantly associated with OC. Both datasets and qRT-PCR demonstrated elevated expression levels of FCGBP and WDR91 in OC. Notably, AFLATOXIN B1 exhibited strong binding affinity to SLC25A45, ciclopirox to BTN3A3, and irinotecan to WDR91. The risk score, age, and stage were identified as independent prognostic factors, with the nomogram displaying efficacy in predicting OC patient survival. Variations in the immune cell infiltration profiles, including naive B cells, and expression levels of 6 immune checkpoint genes, such as CTLA4, were notable. High tumor mutation burden scores were associated with improved survival outcomes. Additionally, significant differences in the IC50 values of 123 anticancer drugs were observed between the 2 risk groups. This findings of this study highlight the efficacy of the efferocytosis-associated risk model in predicting the survival outcomes of OC patients, thus providing a novel reference for prognostic prediction in OC patients.

Humans↗

PubMed2026

In silico analysis based on network pharmacology and biomolecular informatics to explore the mechanism of action of Erjing Pills (from Shengji Zonglu) in the treatment of leukotrichia.

This study aimed to explore the core active ingredients and potential molecular mechanisms of Erjing Pills, a prescription in the classic work of Traditional Chinese Medicine, "Shengji Zonglu," in the treatment of leukotrichia by utilizing network pharmacology and biomolecular docking techniques. The chemical components and potential targets of Chinese herbal medicines were analyzed through databases such as the Traditional Chinese Medicine Systems Pharmacology Database. The targets related to leukotrichia were collected using GeneCards. The intersection targets were obtained using RStudio. The protein-protein interaction (PPI) network map and the "drug-component-target-disease" visualization network were generated using Cytoscape and STRING to screen the core components and key targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were carried out using the Database for Annotation, Visualization and Integrated Discovery and RStudio. Finally, molecular docking verification was performed by AutoDock and PyMOL (Schrödinger LLC). The key active ingredients of Erjing Pills in the treatment of leukotrichia are β-sitosterol, quercetin, baicalein, and stigmasterol. The top 5 PPI core target proteins, in order, are AKT serine/threonine kinase 1, interleukin 6, tumor protein p53, cysteine-aspartic acid protease 3, and interleukin 1 beta. The Gene Ontology enrichment analysis suggests that the biological processes mainly include responses to exogenous stimuli, membrane rafts, and DNA-binding transcription factor binding. The Kyoto Encyclopedia of Genes and Genomes pathways involve signal pathways such as lipid and atherosclerosis, hepatitis B, Kaposi sarcoma virus infection, chemical carcinogenesis, and human cytomegalovirus infection. The molecular docking results indicate that most of the main active ingredients in Erjing Pills have relatively stable binding activities with the key targets, such as AKT serine/threonine kinase 1, interleukin 6, tumor protein p53, cysteine-aspartic acid protease 3, and interleukin 1 beta, in the PPI network. The active ingredients of Erjing Pills may interfere with the pathological process of leukotrichia by regulating key targets and signal pathways. This study provides a theoretical basis for the clinical application of Erjing Pills and indicates the direction for subsequent experimental research.

Drugs, Chinese Herbal↗

PubMed2026

Identification of potential key genes involved in iron deficiency for sepsis: A retrospective cohort and transcriptomic study.

Iron overload has been associated with sepsis, but the role of iron deficiency and its molecular links remain unclear. We investigated the association between iron deficiency and sepsis and identified candidate genes potentially linking these conditions. MIMIC-IV data were used to assess the association between serum iron and sepsis status. Transcriptomic datasets from dietary iron-deficient mice (GSE10421), LPS-induced septic mice (GSE267388), and a human blood sepsis cohort (GSE137340) were sequentially analyzed to identify and externally evaluate candidate genes. IEU Open GWAS summary statistics were used for exploratory Mendelian randomization (MR). Exploratory drug prediction was performed using L1000FWD, followed by molecular docking analysis. Patients with sepsis had significantly lower serum iron levels, and restricted cubic spline analysis showed a nonlinear association between serum iron and the odds of sepsis. Cross-tissue transcriptomic analysis identified Sqle, Lss, and Rdh11 as candidate genes. In the human blood cohort, SQLE and RDH11 were significantly increased, whereas LSS was not significantly altered. Exploratory MR showed that genetically proxied SQLE expression was associated with higher odds of sepsis (odds ratio [OR] = 1.23, P = 1.67 × 10-3), whereas LSS expression was associated with lower odds (OR = 0.97, P = 8.90 × 10-4); RDH11 showed no significant association (OR = 1.01, P = .90). Drug prediction identified ML106 as the top-ranked candidate drug, and molecular docking predicted potential binding poses with SQLE and LSS. Serum iron showed a nonlinear association with sepsis status. SQLE, LSS, and RDH11 emerged as candidate genes, with concordant expression changes of SQLE and RDH11 observed in human blood. MR findings for SQLE and LSS were exploratory and require further validation. ML106 was identified through exploratory drug prediction and requires experimental validation before its therapeutic relevance can be established.

Sepsis↗

PubMed2016

Characterization of Class III Peroxidases from Switchgrass.

Class III peroxidases (CIIIPRX) catalyze the oxidation of monolignols, generate radicals, and ultimately lead to the formation of lignin. In general, CIIIPRX genes encode a large number of isozymes with ranges of in vitro substrate specificities. In order to elucidate the mode of substrate specificity of these enzymes, we characterized one of the CIIIPRXs (PviPRX9) from switchgrass (Panicum virgatum), a strategic plant for second-generation biofuels. The crystal structure, kinetic experiments, molecular docking, as well as expression patterns of PviPRX9 across multiple tissues and treatments, along with its levels of coexpression with the majority of genes in the monolignol biosynthesis pathway, revealed the function of PviPRX9 in lignification. Significantly, our study suggested that PviPRX9 has the ability to oxidize a broad range of phenylpropanoids with rather similar efficiencies, which reflects its role in the fortification of cell walls during normal growth and root development and in response to insect feeding. Based on the observed interactions of phenylpropanoids in the active site and analysis of kinetics, a catalytic mechanism involving two water molecules and residues histidine-42, arginine-38, and serine-71 was proposed. In addition, proline-138 and gluntamine-140 at the 137P-X-P-X140 motif, leucine-66, proline-67, and asparagine-176 may account for the broad substrate specificity of PviPRX9. Taken together, these observations shed new light on the function and catalysis of PviPRX9 and potentially benefit efforts to improve biomass conservation properties in bioenergy and forage crops.

Amino Acid Sequence↗

PubMed2026

Exploring the substrate promiscuity and functional residues of UGT73 family enzymes in Entada phaseoloides.

Flavonoid glycosides and triterpenoid saponins are bioactive plant metabolites with broad applications in food, medicine, and agriculture. These compounds are typically synthesized through glycosylation catalyzed by uridine diphosphate-dependent glycosyltransferases (UGTs). In this study, phylogenetic analysis across multiple species revealed a lineage-specific expansion of the UGT73 family in legumes such as Entada phaseoloides and Glycine max. The genome of the medicinal legume E. phaseoloides was re-annotated using integrated Oxford Nanopore Technologies and Illumina transcriptomic data to identify target genes. Four expanded UGT73 family genes were selected and functionally characterized. UGT73AA6 specifically glycosylates flavonoids, while UGT73CG48 and UGT73CG49 catalyze glycosylation of both flavonoids and pentacyclic triterpenoids. UGT73CG49 exhibits higher catalytic activity for the glucosylation of flavonoids and pentacyclic triterpenes compared to its xylosylation activity. Structural modeling and molecular docking identified key active sites, and site-directed mutagenesis revealed Gly194 as a critical residue enhancing catalytic activity in UGT73CG49. This study provides new insights into the functional evolution and metabolic versatility of the UGT73 family in legumes. The identification and engineering of UGT73 genes from E. phaseoloides lay a foundation for future applications in biosynthetic pathway engineering and the industrial production of high-value glycosides.

Substrate Specificity↗

PubMed2025

From genetic causality to druggable targets: A multiomics framework identifies ZSCAN16 in gout pathogenesis.

ObjectiveGout is a prevalent form of inflammatory arthritis in which many patients respond suboptimally to current therapies. Drug development is hampered by a lack of genetically validated targets, leading to high clinical trial attrition. This study aimed to systematically identify and prioritize novel, druggable targets for gout via a multilayered genetic and functional genomics approach.MethodsWe performed two-sample Mendelian randomization (MR) using cis-expression quantitative trait locus (cis-eQTL) data and dual independent gout genome-wide association study (GWAS) cohorts (openGWAS and FinnGen). The candidate genes were subjected to a rigorous validation pipeline including Bayesian colocalization, phenome-wide association studies (PheWASs) to assess pleiotropy and on-target safety, and single-cell RNA sequencing (scRNA-seq) to delineate the cellular context. Molecular docking was used to evaluate the structural druggability of prioritized targets.ResultsMR analysis revealed 15 genes causally associated with gout. Colocalization analysis (PPH4 > 0.8) prioritized two targets: ZSCAN16 (risk-increasing, OR = 1.04, 95% CI [1.02-1.06]) and TRIM10 (protective, OR = 0.96, 95% CI [0.94-0.98]). Crucially, PheWAS revealed that ZSCAN16 is highly specific to gout, whereas TRIM10 exhibited extensive pleiotropy with hematological and cardiometabolic traits, indicating significant safety risks. Single-cell analysis provided orthogonal validation, demonstrating flare-specific upregulation of ZSCAN16 in cytotoxic T/NK cells. Molecular docking confirmed ZSCAN16 as a structurally druggable target, showing high-affinity binding with known compounds (e.g. digoxin, binding energy = -9.6 kcal/mol).ConclusionsOur study identifies ZSCAN16 as a high-potential, druggable therapeutic target for gout, highlighting its genetic influence on specific immune cell activities during acute flares. Conversely, TRIM10 was deprioritized owing to substantial pleiotropic liabilities and poor chemical tractability. These findings suggest that ZSCAN16 could play a crucial role in the pathogenesis of gout and may provide a valuable lead for future drug discovery efforts.

Humans↗

PubMed2024

Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms.

BACKGROUND: Diabetic nephropathy (DN) is a major contributor to chronic kidney disease. This study aims to identify immune biomarkers and potential therapeutic drugs in DN. METHODS: We analyzed two DN microarray datasets (GSE96804 and GSE30528) for differentially expressed genes (DEGs) using the Limma package, overlapping them with immune-related genes from ImmPort and InnateDB. LASSO regression, SVM-RFE, and random forest analysis identified four hub genes (EGF, PLTP, RGS2, PTGDS) as proficient predictors of DN. The model achieved an AUC of 0.995 and was validated on GSE142025. Single-cell RNA data (GSE183276) revealed increased hub gene expression in epithelial cells. CIBERSORT analysis showed differences in immune cell proportions between DN patients and controls, with the hub genes correlating positively with neutrophil infiltration. Molecular docking identified potential drugs: cysteamine, eltrombopag, and DMSO. And qPCR and western blot assays were used to confirm the expressions of the four hub genes. RESULTS: Analysis found 95 and 88 distinctively expressed immune genes in the two DN datasets, with 14 consistently differentially expressed immune-related genes. After machine learning algorithms, EGF, PLTP, RGS2, PTGDS were identified as the immune-related hub genes associated with DN. In addition, the mRNA and protein levels of them were obviously elevated in HK-2 cells treated with glucose for 24 h, as well as their mRNA expressions in kidney tissues of mice with DN. CONCLUSION: This study identified 4 hub immune-related genes (EGF, PLTP, RGS2, PTGDS), as well as their expression profiles and the correlation with immune cell infiltration in DN.

Diabetic Nephropathies↗

PubMed2025

Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients. METHODS: Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman's correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively. RESULTS: Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target (S100A12) had a high binding affinity (docking score = -10 kcal/mol). CONCLUSION: AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.

Humans↗

PubMed2025

Decoding the PTTG family's contribution to LUAD pathogenesis: a comprehensive study on expression, epigenetics, and therapeutic interventions.

BACKGROUND: Lung adenocarcinoma (LUAD) stands as a prevalent malignancy, yet its pathology remains incompletely comprehended. METHODS: In this comprehensive study, we explored the roles of the pituitary tumor-transforming gene (PTTG) family, including PTTG1, PTTG2, and the pseudogene PTTG3P in lung adenocarcinoma (LUAD). Employing a multi-faceted approach, we conducted in-depth analyses using clinical samples and expression datasets. RESULTS: Our findings revealed a significant up-regulation of PTTG family genes in LUAD cell lines and tissue samples compared to adjacent normal controls, suggesting their potential as diagnostic biomarkers. Through promoter methylation and mutational analyses, we uncovered regulatory mechanisms influencing PTTG gene expression. The exploration of the PTTG family's impact on LUAD patient survival demonstrated their association with adverse outcomes, emphasizing their potential prognostic relevance. Moreover, functional assays demonstrated that the knockdown of PTTG1 and PTTG2 genes resulted in the reduction of cell proliferation, colony formation, and cell migration abilities in A549 and H1975 LUAD cells. Furthermore, our investigation extended to therapeutic avenues, where we identified Calcitriol as a potential drug within the DrugBank database to down-regulate PTTG genes. Molecular docking analyses provided insights into the strong interaction between Calcitriol and PTTG1/PTTG2 proteins, laying the groundwork for further exploration of Calcitriol in LUAD treatment. CONCLUSION: In conclusion, this study contributes a comprehensive understanding of the PTTG family's involvement in LUAD, shedding light on their diagnostic, prognostic, and therapeutic implications.

Humans↗

PubMed2024

An integrated in-silico approach for drug target identification in human pathogen Shigella dysenteriae.

Shigella dysenteriae, is a Gram-negative bacterium that emerged as the second most significant cause of bacillary dysentery. Antibiotic treatment is vital in lowering Shigella infection rates, yet the growing global resistance to broad-spectrum antibiotics poses a significant challenge. The persistent multidrug resistance of S. dysenteriae complicates its management and control. Hence, there is an urgent requirement to discover novel therapeutic targets and potent medications to prevent and treat this disease. Therefore, the integration of bioinformatics methods such as subtractive and comparative analysis provides a pathway to compute the pan-genome of S. dysenteriae. In our study, we analysed a dataset comprising 27 whole genomes. The S. dysenteriae strain SD197 was used as the reference for determining the core genome. Initially, our focus was directed towards the identification of the proteome of the core genome. Moreover, several filters were applied to the core genome, including assessments for non-host homology, protein essentiality, and virulence, in order to prioritize potential drug targets. Among these targets were Integration host factor subunit alpha and Tyrosine recombinase XerC. Furthermore, four drug-like compounds showing potential inhibitory effects against both target proteins were identified. Subsequently, molecular docking analysis was conducted involving these targets and the compounds. This initial study provides the list of novel targets against S. dysenteriae. Conclusively, future in vitro investigations could validate our in-silico findings and uncover potential therapeutic drugs for combating bacillary dysentery infection.

Shigella dysenteriae↗

PubMed2025

Network pharmacology-based study on the mechanism of Tangfukang formula against type 2 diabetes mellitus.

OBJECTIVE: To explore the mechanism of Tangfukang formula (, TFK) in treating type 2 diabetes mellitus (T2DM). METHODS: We employed network pharmacology combined with experimental validation to explore the potential mechanism of TFK against T2DM. Initially, we filtered bioactive compounds with the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Symptom Mapping (SymMap), and gathered targets of TFK and T2DM. Subsequently, we constructed a protein-protein interaction (PPI) network, enriched core targets through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and adopted molecular docking to study the binding mode of compounds and the signaling pathway. Finally, we employed a KKAy mice model to investigate the effect and mechanism of TFK against T2DM. Biochemical assay, histology assay, and Western blot (WB) were used to assess the mechanism. RESULTS: There were 492 bioactive compounds of TFK screened, and 1226 overlapping targets of TFK against T2DM identified. A compound-T2DM-related target network with 997 nodes and 4439 edges was constructed. KEGG enrichment analysis identified some core pathways related to T2DM, including adenosine 5-monophosphate-activated protein kinase (AMPK) signaling pathway. Molecular docking study revealed that compounds of TFK, including citric acid, could bind to the active pocket of AMPK crystal structure with free binding energy of －4.8, －8 and －7.9, respectively. Animal experiments indicated that TFK decreased body weight, fasting blood glucose, fasting serum insulin, homeostasis model of insulin resistance, glycosylated serum protein, total cholesterol, triglyceride, and low-density lipoprotein cholesterol, and improve oral glucose tolerance test results. TFK reduced steatosis in liver tissue, and infiltration of inflammatory cells, and protected liver cells to a certain extent. WB analysis revealed that, TFK upregulated the phosphorylation of AMPK and branched-chain α-ketoacid dehydrogenase proteins. CONCLUSION: TFK has the potential to effectively manage T2DM, possibly by regulating the AMPK signaling pathway. The present study lays a new foundation for the therapeutic application of TFK in the treatment of T2DM.

Diabetes Mellitus, Type 2↗

PubMed2025

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↗

PubMed2026

Exploring the mechanism of the Lianshi Jianpi formula in treating impaired glucose tolerance: a network pharmacology, molecular docking, and experimental validation study.

OBJECTIVE: To explore the bioactive constituents, key targets, signalling pathways, and molecular mechanisms of Lianshi Jianpi formula (, LSJPF) in the treatment of impaired glucose tolerance (IGT) through network pharmacology, molecular docking, and in vivo experiments. METHODS: The active ingredients and targets of LSJPF were identified using the Traditional Chinese Medicine Systems Pharmacology and HERB databases, whereas the IGT-related targets were sourced from GeneCards, DisGeNET, and PubMed. The overlap analysis identified potential targets of LSJPF. Protein-protein interaction networks and core targets were evaluated using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape, and molecular docking confirmed the binding affinities. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using Metascape. The therapeutic mechanisms were validated in an animal IGT model. RESULTS: LSJPF contained 229 compounds, with 15 active compounds and 77 potential target proteins. The phosphatidylinositol-3-kinase (PI3K)-protein kinase B (AKT) signalling pathway emerged as a key IGT pathway. The KEGG enrichment analysis revealed the pivotal genes RAC-alpha serine/threonine-protein kinase (AKT1), heat shock protein 90 kDa alpha B1, and B-cell lymphoma 2 family protein, which predominantly interact with beta-sitosterol and beta-carotene, the major constituents of Semen Euryales, Semen lablab Album, Semen sojae Atricolor in LSJPF. Molecular docking revealed strong binding affinities between LSJPF and IGT-related targets. In an animal IGT model, LSJPF treatment prevented weight loss; reduced food and water intake; decreased blood glucose levels; improved insulin resistance; decreased serum triglyceride, cholesterol, and low-density lipoprotein cholesterol levels; alleviated liver pathology; and significantly increased the levels of phosphorylated adenosine 5'-monophosphate-activated protein kinase (AMPK), PI3K, and AKT, suggesting its potential role in regulating glucose and lipid metabolism. CONCLUSIONS: These findings reveal the potential of LSJPF as an IGT intervention that targets the AMPK/PI3K/AKT cascade, validating network pharmacology predictions and highlighting the role of multipathway mechanisms in metabolic diseases.

Molecular Docking Simulation↗

PubMed2026

Active components and potential mechanisms of Wuzhuyu decoction in the treatment of ethanol-induced acute gastric mucosal injury: a network pharmacology and experimental verification.

OBJECTIVE: To investigate the underlying mechanisms and active components of Wuzhuyu decoction (, WD) in alleviating ethanol-induced acute gastric mucosal injury (GMI) using an integrated approach of network pharmacology and experimental verification. METHODS: Sprague-Dawley rats were randomly divided into six groups: control (Con), model (Mod), bismuth potassium citrate (BPC), WD at low (WD-L), medium (WD-M), and high (WD-H) doses. Following seven days of continuous intragastric administration of the respective treatments, an ethanol-induced gastric mucosal injury model was established in all groups except the control group by oral gavage of anhydrous ethanol. The gastric mucosal injury index was evaluated, and pathological changes were assessed viahematoxylin and eosin (HE) staining. Levels of tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) were measured by enzyme-linked immunosorbent assay (ELISA). The chemical composition was identified by ultra-performance liquid chromatography-tandem mass spectrometry. Active compounds were screened using the Swiss-absorption, distribution, metabolism, and excretion database, and their potential targets were predicted using the Swiss Target Prediction database and bioinformatics annotation database for molecular mechanism. Simultaneously, disease targets related to GMI were retrieved from the online mendelian inheritance in man and GeneCards databases. A protein-protein interaction (PPI) network was constructed, and functional enrichment analyses of gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed using the Metascape database. Key predictions from the network pharmacology analysis were subsequently verified through animal experiments. Protein expression levels of B-cell lymphoma-2 (Bcl-2), Bcl-2-associated X protein (Bax), Cleaved Caspase-3, and Cleaved Caspase-9 were analyzed by Western blot. Finally, molecular docking was performed using AutoDock Vina to investigate the interactions between the active components and core targets. RESULTS: WD treatment significantly reduced the gastric mucosal injury index and the levels of TNF-α, IL-1β, MDA, while it increased the activities of SOD and GSH-Px. Histopathological examination revealed marked improvement in gastric tissue morphology. A total of 145 compounds were identified in WD. Network pharmacology analysis identified 440 overlapping targets between WD and GMI. GO and KEGG enrichment analyses highlighted the apoptosis signaling pathway as a key mechanism for WD's protective effect against ethanol-induced GMI. Experimental validation demonstrated that WD treatment reduced the apoptosis of gastric mucosal epithelial cells, promoted the expression of Bcl-2, and inhibited the expression of Bax, Cleaved Caspase-3 and Cleaved Caspase-9. Molecular docking results indicated that dehydroevodiamine, rutaecarpine, evodiamine, hexahydrocurcumin, and isorhamnetin are potential active components in WD that contribute to the inhibition of apoptosis. CONCLUSIONS: WD alleviates ethanol-induced acute GMI, at least in part, by inhibiting the apoptosis. The primary active components responsible for this effect are dehydroevodiamine, rutaecarpine, evodiamine, hexahydrocurcumin, and isorhamnetin.

Drugs, Chinese Herbal↗

PubMed2026

Uncovering ShuangZi Powder's Anti-Ovarian Cancer Mechanism: A Systems Biology and Experimental Approach.

INTRODUCTION: This study investigated the anti-ovarian cancer (OC) effects of Shuangzi Powder (SZP) and its regulatory impact on the tumor microenvironment. METHOD: This study employed systems biology approaches, integrating molecular docking and experimental validation, to explore the pharmacological mechanisms of SZP in OC treatment. To identify potential bioactive compounds and target genes of SZP, network pharmacology, protein- protein interaction network analysis,.Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were conducted. RESULTS: Among the 11 bioactive ingredients identified in SZP, 1,767 potential therapeutic targets were predicted, while 2,637 differentially expressed genes were found to be associated with OC. KEGG pathway analysis revealed significant enrichment in pathways related to cancer, apoptosis, the PI3K-Akt signaling pathway, and the PD-L1/PD-1 checkpoint pathway. Treatment of A2780 cells with β,β-Dimethylacrylshikonin (DMAS) inhibited cell viability, migration, and invasion. Moreover, DMAS downregulated the expression of cell cycle- and apoptosis-related genes (CCNB1, CHEK1, CCNE1, and PARP1) and upregulated the immune checkpoint gene PD-L1. DISCUSSION: These findings indicate that multiple components, targets, and pathways are involved in OC treatment by SZP. CONCLUSION: DMAS, one of the bioactive ingredients of SZP, was predicted and preliminarily validated to exert inhibitory effects on OC cells, mainly through the regulation of the cell cycle, apoptosis, and immune response, as demonstrated by molecular docking and experimental analyses.

Ovarian Neoplasms↗

PubMed2025

Genomic mapping of diabetic kidney disease biomarkers and identification of potential inhibitors through virtual screening.

BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus, marked by a multifactorial pathogenesis and the absence of sensitive diagnostic biomarkers. Identifying novel molecular targets and therapeutic options is essential to improve early diagnosis and treatment outcomes. METHODS: To uncover potential biomarkers and therapeutic candidates, we performed an integrated genomic analysis using microarray and RNA-seq datasets from the Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) databases. Differentially expressed genes (DEGs) were identified and subjected to protein-protein interaction (PPI) network analysis. Key genes were further explored through virtual screening of an FDA-approved compound library using molecular docking techniques. Drug-likeness was assessed via Lipinski's rule of five. RESULTS: A total of 40 DEGs were identified, among which ISCU (downregulated; involved in iron-sulfur cluster biogenesis) and AP1S2 (upregulated; associated with vesicular trafficking) emerged as potential biomarkers. PPI analysis revealed their involvement in critical DKD-related pathways, such as extracellular matrix remodeling and oxidative stress. Virtual screening identified six FDA-approved compounds with high binding affinity (≤-7.96 kcal/mol) to ISCU, notably ZINC000001576020, all of which complied with Lipinski's rule. CONCLUSIONS: This in-silico study nominates ISCU and AP1S2 as candidate diagnostic biomarkers for DKD and identifies computationally prioritized inhibitors targeting ISCU. These findings require experimental validation but provide a molecular framework for precision diagnosis and therapeutic development. These findings offer new molecular insights that could inform precision diagnosis and personalized treatment strategies for diabetic kidney disease.

Diabetic Nephropathies↗

PubMed2026

Comparative Characterization of σ32-Dependent Promoters for the Heat-Inducible Expression of FAST-PETase in Escherichia coli.

Efficient regulation of recombinant enzyme expression is an important consideration for the development of microbial biocatalysts. Heat-inducible promoters regulated by the alternative sigma factor σ32 provide an inducer-free strategy for controlling gene expression in Escherichia coli. In this study, four σ32-dependent promoters (PdnaK, PgrpE, PibpA, and PclpB) were comparatively characterized using the PET-degrading enzyme FAST-PETase fused to superfolder green fluorescent protein as a model recombinant protein. Promoter performance was evaluated based on basal leakage, induction kinetics, and expression strength following heat induction. Among the promoters examined, PdnaK exhibited the strongest heat-inducible expression and was dissected to examine the autonomous and combinatorial behavior of its promoter-derived elements. Molecular docking analysis further supported the experimental observations by showing qualitative agreement between predicted σ32-DNA interactions and promoter performance. Together, these findings provide a comparative characterization of σ32-dependent promoters and identify promoter architectures that may facilitate the development of heat-inducible recombinant enzyme expression systems in E. coli.

Escherichia coli↗

PubMed2026

Domesticated Argania spinosa in Eastern Morocco: HPLC-DAD/GC-MS Chemical Profiling, Antioxidant and Antidiabetic Activities, and Network Pharmacology-Guided Molecular Docking.

The argan tree (Argania spinosa) is an endemic Moroccan species known for its primary product, argan oil, which possesses exceptional nutritional and medicinal properties. The current study aimed to evaluate and compare the antidiabetic and antioxidant activities of argan oil obtained from the introduced and native argan tree in eastern Morocco, to analyze its chemical composition using HPLC-DAD and GC-MS, and to investigate the molecular mechanisms behind the obtained pharmacological activities through an in silico pharmacological networking and molecular docking study. The results revealed that argan oil from all three regions of Morocco (Oujda, Agadir, and Chouihya) is rich in oleic and linoleic acids as major constituents, along with the presence of significant tocopherols. Regarding the antioxidant assays, including DPPH radical scavenging and iron-reducing power tests, argan oil from Oujda exhibited the highest activity, with the lowest IC50 values of 15.25 ± 0.022 mg/mL and 28.5 ± 1.7 mg/mL, respectively. Concerning the antidiabetic activity, we found that oil from Chaouihya showed the strongest α-amylase inhibition, while Oujda oil had the highest antiglycation activity, indicating that even introduced argan trees retain potent bioactivity. The results of the in silico investigation suggested that tocopherols may contribute to the antioxidant and antidiabetic potential of argan oil, showing predicted antioxidant activity (Pa = 0.843-0.967) and favorable binding affinities toward iNOS (ΔG = -9.3 kcal mol-1) and α-glucosidase (ΔG = -8.2 kcal mol-1). The identified fatty acids also showed predicted insulin-promoting activity (Pa = 0.59-0.75) and moderate enzyme-binding potential. Pharmacological network analysis identified 51 shared genes associated with antioxidant, antidiabetic, and argan-related targets, with enrichment of the AGE-RAGE signaling pathway. These computational findings provide possible molecular associations that may help explain the observed biological activities, although they remain predictive and require experimental validation. Overall, the in silico analysis suggests that tocopherols could be among the contributors to the multi-target profile of Argania spinosa oil, while fatty acids may provide complementary effects related to glycemic regulation.

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