Food safety analysis: network toxicology, molecular docking, machine learning and single-cell analysis to interpret sodium benzoate-induced renal injury from multiple perspectives

Sodium benzoate, a common food additive, has raised safety concerns despite its general recognition as safe. This study aimed to investigate the mechanisms of sodium benzoate-induced nephrotoxicity. A network toxicology approach was used to identify key targets and core pathways involved in sodium benzoate nephrotoxicity. Molecular docking validated the binding affinity between these targets and sodium benzoate. Machine learning and single-cell analysis further explored the underlying mechanisms using dataset validation. Protein-protein interaction (PPI) network analysis revealed five key targets with the lowest binding energies (Matrix metalloproteinase 2 (MMP2), Estrogen Receptor 1 (ESR1), Poly (ADP-ribose) polymerase 1 (PARP1), Prostaglandin-endoperoxide synthase 2 (PTGS2), Mitogen-activated protein kinase 14 (MAPK14)) as central to sodium benzoate-induced renal injury. Enrichment analysis indicated 'diabetic nephropathy' (DN) as the primary pathway. Machine learning and single-cell analysis confirmed PTGS2 as the dominant factor exerting nephrotoxicity among the key genes. This multi-method study uncovered potential mechanisms of sodium benzoate-induced renal injury, providing a basis for improving food safety evaluations.