Issue #19: Integrative Network Pharmacology and Molecular Docking-Based Validation of Berberine as a Therapeutic Agent in Arsenic-Induced Cardiotoxicity.

Signal of the Day

Integrative Network Pharmacology and Molecular Docking-Based Validation of Berberine as a Therapeutic Agent in Arsenic-Induced Cardiotoxicity.

Exposure to arsenic (As) is a serious environmental and public health risk because it can cause systemic toxicity, which could lead to serious cardiovascular disease like heart failure, arrhythmias, and coronary heart disease (CHD). Exploring safer and multi-target therapeutic agents is gaining popularity as a result of the shortcomings of traditional therapies. The isoquinoline alkaloid berberine which is derived from plants, exhibits strong anti-inflammatory, antioxidant, and cardioprotective properties. This study employs an integrated network pharmacology and molecular docking approach to investigate the molecular mechanisms and therapeutic potential of berberine in arsenic-induced cardiotoxicity. Key genes target arsenic-induced cardiotoxicity and berberine, have been identified using the Swiss Target Prediction, Gene Cards, OMIM, and CTD databases. A protein-protein interaction (PPI) network was generated by analysing frequently intersecting genes with the STRING and Cytoscape tools. Shiny GO was used to conduct pathway enrichment analysis for the KEGG and Gene Ontology databases. Auto Dock was used to assess berberine’s binding affinity. Berberine and arsenic-related cardiotoxicity shared 17 common targets. The primary targets were identified using Cytoscape ABL-1 (2G2F), CDK2 (1HCK), CYP19A1 (3EQM), ICAM-1 (4G6J), KIT (1T45), MAPK14 (3PY3), PGR (1A28), PTGS2 (5F19), RAC1 (3TH5), and SRC (2SRC). Enrichment analysis revealed TNF, VEGF, and AGE-RAGE signaling involvement, all of which are linked to oxidative stress, inflammation, and endothelial dysfunction. Binding affinity between berberine and the target was found to be ABL-1 (-9.2 kcal/mol), PTGS2 (-8.8 kcal/mol), SRC (-8.7 kcal/mol), CYP19A1 (-8.6 kcal/mol), KIT (-8.3 kcal/mol), RAC1 (-7.9 kcal/mol), CDK2 (-7.5 kcal/mol), ICAM-1 (-7.2 kcal/mol), MAPK (-6.8 kcal/mol), PGR (-5.6 kcal/mol). Berberine has multi-targeted therapeutic potential for arsenic-induced cardiotoxicity by modulating inflammatory and oxidative pathways. These results could support the possible usage of berberine in the treatment of cardiovascular diseases caused by arsenic and provide a mechanistic link for further experimental validation.

Why this matters: Enhances small-molecule or peptide docking accuracy for targeted drug discovery.

Also Worth Reading

Mechanisms of cellular senescence combined with molecular docking strategies: A biomarker study of potential therapeutic targets for allergic rhinitis.

Bioinformatics and molecular docking methods were used to screen potential biomarkers of cellular senescence in allergic rhinitis (Allergic rhinitis AR), which provided a theoretical basis for revealing the mechanism of AR and exploring new therapeutic approaches. Four AR-related gene chips (GSE19187, GSE43523, GSE44037, and GSE51392) were downloaded from the gene expression database (GEO) for data pooling. Screening differential genes (DEGs) were taken to intersect with cellular senescence-related genes (SRGs) to obtain differential senescence genes (DESRGs). The differential senescence genes were subjected to Gene Ontology Database (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and GSEA enrichment analysis. Protein-protein interaction (PPI) networks were constructed through the STRING database, MCODE plugin weights were analyzed to identify important gene cluster modules, and Hub genes were screened using the CytoHubba plugin topological network algorithm. Hub gene protein interactions network (GeneMANIA) was constructed by the GeneMANIA database. Predict Hub gene construct mRNA-miNA-lncRNA interactions by miRanda, miRDB, miRWalk, TargetScan, and spongeScan databases; construct Hub gene transcription factor regulatory networks by TRRUST database; analyze Hub gene-drug interactions by DGIdb database and select commonly used drugs in the clinic for molecular docking validation. A total of 264 differential genes were screened in the training set with corrected P.adj < 0.05 and |log2FC| ≥ 1.2 as the filtering condition, and a total of 866 cellular senescence genes, and 20 differential senescence genes (DESRGs) were obtained by taking the intersection of the two. A total of 19 Hub genes were obtained after PPI analysis, which were CCL2, STAT1, TLR2, IGFBP3, TLR3, KLF4, IL1RN, IRF1, SERPINB2, DPP4, MME, NQO1, SAMHD1, XAF1, PHGDH, EIF4EBP1, CTH, HSPA2, AHR The gene-protein interaction network identified 19 Hub genes associated with 21 functional proteins. 5 of the Hub gene loci were associated with 29 miRNAs and 53 lncRNAs. The transcription factor regulatory network obtained 15 transcription factors capable of regulating Hub genes. The analysis of drug-gene interactions identified 489 drugs that target hub genes. For example, in the case of budesonide, the interacting genes STAT1, TLR2, TLR3, and AHR were selected for molecular docking. Similarly, for mometasone, the interacting genes TLR2 and CTH were chosen for molecular docking. Mining AR-related Hub senescence genes by bioinformatics analysis, constructing PPI network, ceRNA network, transcription factor regulatory network, gene-drug interaction network and molecular docking validation, we screened 19 CCL2, STAT1, TLR2, IGFBP3, TLR3, KLF4, IL1RN, IRF1, SERPINB2, DPP4, MME, NQO1, SAMHD1, XAF1, PHGDH, EIF4EBP1, CTH, HSPA2, and AHR are expected to be Hub genes for potential diagnostic and therapeutic biomarkers, which will provide targets and new insights for further in-depth explorations of AR cellular senescence-related mechanisms of action and therapy.

Integrating machine learning and molecular docking to elucidate the mechanism of atrial fibrillation induced by di(2-ethylhexyl) phthalate.

Environmental exposure is closely associated with the development of cardiovascular diseases. This study aims to explore the molecular mechanism by which Di (2-ethylhexyl) phthalate (DEHP) induces atrial fibrillation (AF). AF-related target genes were identified through differential expression analysis of multiple datasets. Machine learning algorithms, Weighted Gene Co-expression Network Analysis (WGCNA), Machine learning (ML) and molecular docking technology were integrated to investigate the binding interaction between DEHP and target proteins. A total of 8 potential key targets (ITGB2, ARPC1B, RYR2, FPR2, MPEG1, PRKCD, LCP1, RAC2) involved in DEHP-induced AF were identified. ML analysis confirmed these genes as core regulatory genes, among which ITGB2, ARPC1B, and RYR2 exhibited high diagnostic potential (Area Under the Receiver Operating Characteristic Curve, AUC ≥ 0.85). Molecular docking simulations showed stable binding specificity between DEHP and these core targets, with binding energies all below -3 kcal/mol. DEHP may promote AF pathogenesis by targeting specific genes and signaling pathways. DEHP has high binding affinity with ITGB2, ARPC1B, and RYR2, which may serve as targets for future interventions. These findings provide important insights into the in-depth exploration of the mechanism underlying DEHP-induced AF.

Mechanistic study of plastic monomers in gestational diabetes mellitus: A network toxicology and molecular docking approach.

Plastics are widely used in various fields such as food packaging, textile fibers, building materials, and transportation. Although the relationship between plastic additives and diseases has been reported, there is limited research on the association between plastic monomers (PM) and gestational diabetes mellitus (GDM). This study aims to investigate the link between environmental PM and GDM. By employing advanced network toxicology and molecular docking techniques, we successfully elucidated the molecular mechanisms by which PM may induce GDM. Utilizing databases such as PubChem, SEA, Super-PRED, SwissTargetPrediction, PharmMapper, Gene Cards, and OMIM, we identified potential targets associated with the disease. Further analysis using STRING and Cytoscape software helped determine the core targets most significantly related to these metabolic disorders. Additionally, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted using the David database to characterize these core targets. Finally, molecular docking with CB-Dock2 was used to validate the binding affinity of PM to these target proteins. Our findings suggest that PM may potentially induce GDM by modulating the insulin signaling pathway through STAT3, AKT1, and TP53. In summary, this work provides novel insights into the mechanisms by which environmental pollutants may trigger GDM, thereby laying a theoretical foundation for disease prevention and treatment. It offers valuable references for the safety evaluation of plastics, urging food safety regulatory agencies to strengthen oversight and encouraging the public to reduce plastic usage.

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