Emerging plasticizer induced lipid metabolism disorders revealed by network toxicology molecular docking and dynamics simulation

The toxic side effects of acetyl tributyl citrate (ATBC) on humans are concerning, but studies related to its effects on osteoarthritis (OA) are lacking. Therefore, this study aimed to explore the potential targets and mechanisms of action of ATBC in OA through network toxicology. We obtained ATBC-related targets from the ChEMBL, Swiss Target Prediction, and STITCH databases and OA-related targets from the GeneCards, DisGeNET, and OMIM databases and identified overlapping targets. Core targets (key molecules in the progression of diseases) were determined via the STRING database and Cytoscape software, followed by further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to determine potential mechanisms in depth. Moreover, a gene interaction and competing endogenous RNA (ceRNA) network for the core targets was constructed. Additionally, the expression levels of the core targets were preliminarily validated using single-cell data from the GEO database. Furthermore, in-depth validation of the core targets was carried out through molecular docking and molecular dynamics simulations. A total of 132 overlapping targets between ATBC and OA were identified, and six core targets (TP53, EZH2, HDAC1, HDAC2, SIRT1, and SMARCA4) were further screened. The results of the enrichment analysis revealed that the core pathways related to the effect of ATBC on OA mainly involved key signaling cascades, including the thyroid hormone signaling pathway, the Notch signaling pathway, and cellular senescence. Single-cell analysis revealed that the core target is expressed in different cell subpopulations. Molecular docking and molecular dynamics simulation results indicate that there is a stable binding interaction between ATBC and the core target. This study provides a theoretical foundation for the molecular mechanisms of OA triggered by ATBC, highlighting the value of network toxicology in assessing the toxicity of emerging environmental pollutants. However, further clinical and experimental investigations are needed to validate these findings.

The rising prevalence of Acetyl Tributyl Citrate (ATBC) as an environmental pollutant has raised considerable concern about its potential role in oral diseases. This study focuses on the effects of ATBC exposure on oral squamous cell carcinoma (OSCC), with the specific aim of identifying potential targets and elucidating the associated molecular mechanisms, employing network pharmacology, molecular docking, and molecular dynamics (MD) simulation. Relevant targets of OSCC were collected from the TTD, GeneCards, and OMIM databases. The ChEMBL, STITCH, TargetNet, and Swiss Target Prediction databases were utilized to screen ATBC compounds and identify associated compound targets. We selected 107 potential targets for ATBC-induced OSCC and extracted 22 core targets using STRING 12.0 and Cytoscape 3.9.1, including AKT1, HSP90AA1, ESR1, CASP3, BCL2, PPARG, MMP9, and EGFR. Gene ontology (GO) analysis revealed that ATBC-induced OSCC was associated with cell proliferation and apoptosis caused by exogenous chemicals. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that ATBC participates in the cancer signaling pathway through Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1), Epidermal Growth Factor Receptor (EGFR), and Matrix Metalloproteinase-9 (MMP9). Molecular docking and MD simulations indicate the high stability and reliability of ATBC binding to these core targets. This study elucidates the role of ATBC in the induction of OSCC and its underlying molecular mechanisms, offering significant support for uncovering the toxicological mechanisms of ATBC. Moreover, it provides a theoretical foundation for developing preventive strategies and therapeutic interventions for oral diseases associated with ATBC exposure.

Integrated network toxicology and experimental validation reveal nephrotoxic effects of acetyl tributyl citrate in HK-2 cells.

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies.

BackgroundAcetyl tributyl citrate (ATBC) is a widely used environmental plasticizer that has raised concerns regarding its potential health effects, particularly its role in cancer development. Although ATBC is generally considered to have a safer profile compared to traditional phthalate-based plasticizers, research on its association with bone cancer remains limited. The aim of this study is to elucidate the complex effects of Acetyl tributyl citrate (ATBC) on bone cancer and to unravel the potential molecular mechanisms by which environmental pollutants influence the disease process.MethodsThis study utilized multiple online databases to identify target genes associated with ATBC and bone cancer. Initially, protein–protein interaction (PPI) analysis and visualization of the intersecting genes were performed. Subsequently, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted to explore the underlying mechanisms connecting the two conditions. Finally, molecular docking was employed to validate the interactions between these compounds and their respective targets.ResultsUsing the CHEMBL, SwissTarget Prediction, and TargetNet databases, we screened 193 genes associated with ATBC. Additionally, we identified 4,439 genes related to bone cancer through the GeneCards, OMIM, and TTD databases, resulting in 73 intersecting genes. After rigorous refinement utilizing the STRING platform and Cytoscape software, we identified five core targets: STAT3, EGFR, MMP9, MAPK1, and MMP2. Functional enrichment analysis indicated that the core targets of ATBC’s influence on bone cancer are primarily involved in the regulation of apoptosis, carcinogenesis, and cellular proliferation, among other biological processes. Finally, molecular docking simulations conducted with AutoDock confirmed robust binding interactions between ATBC and these core targets, thereby enhancing our understanding of their interactions.ConclusionThis study underscores the potential carcinogenic effects of ATBC in bone cancer, identifying key targets such as STAT3, EGFR, MMP9, MAPK1, and MMP2. The findings indicate that ATBC may facilitate the progression of bone cancer by targeting essential signaling pathways and remodeling the tumor microenvironment. This emphasizes the necessity for further research into the environmental risks associated with this plasticizer.

Effect of Acetyl tributyl citrate on bone metabolism based on network toxicology and molecular docking technology

Efficient analysis of toxicity and mechanisms of Acetyl tributyl citrate on aging with network toxicology and molecular docking strategy

Network toxicology, molecular docking, and molecular dynamics simulations revealed the effects of carbendazim on bone metabolism and identified potential treatment drugs.

Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Cyperus rotundus (CR) is widely used in traditional Chinese medicine to prevent and treat a variety of diseases. However, its functions and mechanism of action in osteoarthritis (OA) has not been elucidated. Here, a comprehensive strategy combining network pharmacology, molecular docking, molecular dynamics simulation and in vitro experiments was used to address this issue. The bioactive ingredients of CR were screened in TCMSP database, and the potential targets of these ingredients were obtained through Swiss Target Prediction database. Genes in OA pathogenesis were collected through GeneCards, OMIM and DisGeNET databases. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using DAVID database. STRING database and Cytoscape 3.10 software were used to construct "component-target-pathway" network, and predict the core targets affected by CR. The binding affinity between bioactive components and the core targets was evaluated by molecular docking and molecular dynamics simulation. The therapeutic activity of kaempferol on chondrocytes in inflammatory conditions was verified by in vitro experiments. Fifteen CR bioactive ingredients were obtained, targeting 192 OA-related genes. A series of biological processes, cell components, molecular functions and pathways were predicted to be modulated by CR components. The core targets of CR in OA treatment were AKT serine/threonine kinase 1 (AKT1), interleukin 1 beta (IL1B), SRC proto-oncogene, non-receptor tyrosine kinase (SRC), BCL2 apoptosis regulator (BCL2), signal transducer and activator of transcription 3 (STAT3), epidermal growth factor receptor (EGFR), hypoxia-inducible factor 1 subunit alpha (HIF1A), matrix metallopeptidase 9 (MMP9), estrogen receptor 1 (ESR1) and PPARG orthologs from vertebrates (PPARG), and the main bioactive ingredients of CR showed good binding affinity with these targets. In addition, kaempferol, one of the CR bioactive components, weakens the effects of IL-1β on the viability, apoptosis and inflammation of chondrocytes. Theoretically, CR has great potential to ameliorate the symptoms and progression of OA, via multiple components, multiple targets, and multiple downstream pathways.

BackgroundArtificial sweeteners (AS) have been widely utilized in the food, beverage, and pharmaceutical industries for decades. While numerous publications have suggested a potential link between AS and diseases, particularly cancer, controversy still surrounds this issue. This study aims to investigate the association between AS consumption and cancer risk.MethodsTargets associated with commonly used AS were screened and validated using databases such as CTD, STITCH, Super-PRED, Swiss Target Prediction, SEA, PharmMapper, and GalaxySagittarius. Cancer-related targets were sourced from GeneCards, OMIM, and TTD databases. AS-cancer targets were identified through the intersection of these datasets. A network visualization (‘AS-targets-cancer’) was constructed using Cytoscape 3.9.0. Protein–protein interaction analysis was conducted using the STRING database to identify significant AS-cancer targets. GO and KEGG enrichment analyses were performed using the DAVID database. Core targets were identified from significant targets and genes involved in the ‘Pathways in cancer’ (map05200). Molecular docking and dynamics simulations were employed to verify interactions between AS and target proteins. Pan-cancer and univariate Cox regression analyses of core targets across 33 cancer types were conducted using GEPIA 2 and SangerBox, respectively. Gene chip datasets (GSE53757 for KIRC, GSE21354 for LGG, GSE42568 for BRCA, and GSE46602 for PRAD) were retrieved from the GEO database, while transcriptome and overall survival data were obtained from TCGA. Data normalization and identification of differentially expressed genes (DEGs) were performed on these datasets using R (version 4.3.2). Gene Set Enrichment Analysis (GSEA) was employed to identify critical pathways in the gene expression profiles between normal and cancer groups. A cancer risk prognostic model was constructed for key targets to further elucidate their significance in cancer initiation and progression. Finally, the HPA database was utilized to investigate variations in the expression of key AS-cancer target proteins across KIRC, LGG, BRCA, PRAD, and normal tissues.ResultsSeven commonly used AS (Aspartame, Acesulfame, Sucralose, NHDC, Cyclamate, Neotame, and Saccharin) were selected for study. A total of 368 AS-cancer intersection targets were identified, with 48 notable AS-cancer targets, including TP53, EGFR, SRC, PIK3R1, and EP300, retrieved. GO biological process analysis indicated that these targets are involved in the regulation of apoptosis, gene expression, and cell proliferation. Thirty-five core targets were identified from the intersection of the 48 significant AS-cancer targets and genes in the 'Pathways in cancer' (map05200). KEGG enrichment analysis of these core targets revealed associations with several cancer types and the PI3K-Akt signaling pathway. Molecular docking and dynamics simulations confirmed interactions between AS and these core targets. HSP90AA1 was found to be highly expressed across the 33 cancer types, while EGF showed the opposite trend. Univariate Cox regression analysis demonstrated strong associations of core targets with KIRC, LGG, BRCA, and PRAD. DEGs of AS-cancer core targets across these four cancers were analyzed. GSEA revealed upregulated and downregulated pathways enriched in KIRC, LGG, BRCA, and PRAD. Cancer risk prognostic models were constructed to elucidate the significant roles of key targets in cancer initiation and progression. Finally, the HPA database confirmed the crucial function of these targets in KIRC, LGG, BRCA, and PRAD.ConclusionThis study integrated data mining, machine learning, network toxicology, molecular docking, molecular dynamics simulations, and clinical sample analysis to demonstrate that AS increases the risk of kidney cancer, low-grade glioma, breast cancer, and prostate cancer through multiple targets and signaling pathways. This paper provides a valuable reference for the safety assessment and cancer risk evaluation of food additives. It urges food safety regulatory agencies to strengthen oversight and encourages the public to reduce consumption of foods and beverages containing artificial sweeteners and other additives.

Mechanistic decoding of octyl methoxycinnamate-induced breast toxicity via network toxicology, mendelian randomization, and molecular simulations.

1,4-Naphthoquinone scaffold-derived compounds has shown considerable pharmacological properties against cancer, including acute myeloid leukemia (AML) However, its impact and mechanisms in AML are uncertain. In this study, the mechanisms of 1,4-naphthoquinone scaffold-derived compounds against AML were investigated via network pharmacology, molecular docking and molecular dynamics simulation. ASINEX database was used to collect the 1,4-naphthoquinone scaffold-derived compounds, and compounds were extracted from the software to evaluate their drug similarity and toxicity. The potential targets of compounds were retrieved from the SwissTargetPrediction Database and the Similarity Ensemble Approach Database, while the potential targets of AML were obtained from the GeneCards databases and Gene Expression Omnibus. The STRING database was used to construct a protein–protein interaction (PPI) network, topologically and Cyto Hubb plugin of Cytoscape screen the central targets. After selecting the potential key targets, the gene ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the intersection targets, and a network map of “compounds-potential targets-pathway-disease” were constructed. Molecular docking of the compounds with the core target was performed, and core target with the strongest binding force and 1,4-naphthoquinone scaffold-derived compounds was selected for further molecular dynamics simulation and further molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) approach verification. In addition, the Bloodspot database was applied to perform the overall survival of core targets. A total of 19 1,4-naphthoquinone scaffold-derived compounds were chosen out, and then 836 targets of compounds, 96 intersection targets of AML were screened. Core targets include STAT3, TLR4, HSP90AA1, JUN, MMP9, PTPRC, JAK2, PTGS2, KIT and CSF1R. GO functional enrichment analysis revealed that 90 biological processes, 10 cell components and 12 molecular functions were enriched while KEGG pathway enrichment analysis revealed 34 enriched signaling pathways. Analysis of KEGG enrichment hinted that these 10 core genes were located in the pathways in cancer, suggesting that 1,4-naphthoquinone scaffold-derived compounds had potential activity against AML. Molecular docking analysis revealed that the binding energies between 1,4-naphthoquinone scaffold-derived compounds and the core proteins were all higher than − 6 kcal/mol, indicating that the 10 core targets all had strong binding ability with compounds. Moreover, a good binding capacity was inferred from molecular dynamics simulations between compound 7 and MMP9. The total binding free energy calculated using the MM/GBSA approach revealed values of − 6356.865 kcal/mol for the MMP9-7 complex. In addition, Bloodspot database results exhibited that HSP90AA1, MMP9 and PTPRC were associated with overall survival. The findings provide foundations for future studies into the interaction underlying the anti-AML potential of compounds with 1,4-naphthoquinone-based scaffold structures. Compounds with 1,4-naphthoquinone-based scaffold structures exhibits considerable potential in mitigating and treating AML through multiple targets and pathways.

Objectives Perfluorooctanoic acid (PFOA), widely used in food-contact materials, industrial coatings, and other applications, enters the food chain via air, soil, and water, posing a potential public health risk. Methods This study employs network toxicology, Mendelian randomization, molecular docking and molecular dynamics simulation to preliminarily elucidate the mechanisms by which PFOA’s toxic targets contribute to renal impairment. Through integrated analysis of multi-database bioinformatics, we identified 85 cross-targets associated with PFOA-induced renal toxicity. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed significant enrichment of these targets in pathways related to ribosomes, lysosomes, complement and coagulation cascades, steroid hormone metabolism, immune-inflammatory diseases, and drug metabolism. STRING and Cytoscape tools identified five core targets (CYP3A4, CASP3, REN, PPARG, and IL-10). Mendelian randomization confirmed IL-10 as a central mediator of PFOA’s nephrotoxicity. Molecular docking and molecular dynamics simulation demonstrated a high binding affinity between PFOA and IL-10. Results Our findings suggest that PFOA likely exacerbates renal injury by suppressing IL-10 expression, thereby amplifying inflammatory responses, accelerating renal cell damage and fibrosis, and ultimately impairing kidney function. Conclusion This study elucidates the molecular mechanisms underlying PFOA-induced nephrotoxicity, offering novel insights for environmental health research.

Network toxicology and machine learning reveal the impact of acetyl tributyl citrate on erectile dysfunction and identify cathepsin S as a critical regulator.