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Published on: January 12, 2024
Exploring the Toxicological Effects of Acetyl Tributyl Citrate Exposure on Osteoarthritis Based on Machine Learning,
Yifang Zhu1, Lin Deng1, Junxiang Xia1
1Clinical Laboratory, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, People's Republic of China.
Objective:
To investigate the potential toxicological effects of acetyl tributyl citrate (ATBC) on osteoarthritis (OA) and elucidate the underlying mechanisms using bioinformatics, machine learning, and network toxicology.
Methods:
ATBC targets were identified from multiple databases, and OA-associated differentially expressed genes (DEGs) were sourced from GSE51588. Intersection analysis identified common targets. Functional enrichment and protein-protein interaction (PPI) network analysis were performed. Machine learning algorithms (LASSO, Random Forest and SVM) validated core targets, with ROC curves assessing diagnostic potential. Immune infiltration differences were analyzed via Cibersort. Molecular docking confirmed ATBC binding to core targets, and an adverse outcome pathway (AOP) framework was developed to elucidate ATBC's role in exacerbating OA through key genes and pathways.
Results:
Intersection analysis identified 40 common targets related to both ATBC and OA. Functional enrichment analysis revealed that these targets were significantly involved in calcium signaling pathways and neuroactive ligand-receptor interactions, both of which are implicated in OA pathogenesis. The PPI network analysis identified TNF, MMP8, CXCR4, and SLC2A1 as core targets. Machine learning algorithms further validated these core targets. ROC curve analysis showed that these genes have diagnostic potential, with AUC values ranging from 0.762 to 0.970. Immune infiltration analysis using Cibersort revealed significant differences in immune cell infiltration between OA and control groups, with core targets showing distinct correlations with various immune cells. Molecular docking confirmed strong binding affinities between ATBC and the core targets, with binding energies less than -5 kcal/mol. A novel adverse outcome pathway (AOP) framework was established, suggesting that ATBC may influence the expression of TNF, CXCR4, MMP8, and SLC2A1, with the calcium signaling and neuroactive ligand-receptor interaction pathways potentially contributing to immune dysregulation and OA progression.
Conclusion:
The identification of key targets (TNF, MMP8, CXCR4, and SLC2A1) and molecular docking results elucidates potential mechanisms by which ATBC exposure may exacerbate OA progression. The AOP provides evidence for joint-health risk assessment of plasticizers and offers readily measurable biomarkers for regulatory toxicology and future therapeutic development.
Insights
Acetyl tributyl citrate (ATBC) may worsen osteoarthritis (OA) by affecting key genes like TNF and MMP8. This study identifies potential biomarkers for risk assessment and therapeutic development in plasticizer toxicology.
Area of Science:
- Toxicology
- Bioinformatics
- Computational Biology
Background:
- Osteoarthritis (OA) is a degenerative joint disease with complex pathogenesis.
- Plasticizers, such as acetyl tributyl citrate (ATBC), are widely used and their potential health effects require investigation.
- Understanding the molecular mechanisms underlying ATBC's impact on OA is crucial for risk assessment.
Purpose of the Study:
- To investigate the toxicological effects of ATBC on osteoarthritis (OA).
- To elucidate the underlying mechanisms of ATBC-induced OA exacerbation using bioinformatics, machine learning, and network toxicology.
- To identify potential biomarkers for OA risk assessment related to ATBC exposure.
Main Methods:
- Identified ATBC targets and OA-associated differentially expressed genes (DEGs).
- Performed intersection analysis, functional enrichment, and protein-protein interaction (PPI) network analysis.
- Utilized machine learning (LASSO, Random Forest, SVM) for target validation, ROC analysis for diagnostic potential, Cibersort for immune infiltration analysis, and molecular docking.
Main Results:
- Identified 40 common targets between ATBC and OA, significantly enriched in calcium signaling and neuroactive ligand-receptor interactions.
- Validated TNF, MMP8, CXCR4, and SLC2A1 as core targets with diagnostic potential (AUC 0.762-0.970).
- Confirmed ATBC binding to core targets and established an adverse outcome pathway (AOP) framework linking ATBC to OA progression via immune dysregulation.
Conclusions:
- Key targets (TNF, MMP8, CXCR4, SLC2A1) and molecular docking results elucidate ATBC's role in exacerbating OA.
- The developed AOP framework supports joint-health risk assessment of plasticizers.
- Identified biomarkers offer potential for regulatory toxicology and therapeutic development.
