مشخصات پژوهش

In this study, we examined 43 Lactiplantibacillus plantarum isolates from Iranian traditional dairy products to elucidate the correlation between plantaricin gene profiles and antimicrobial activity. Twelve target pln genes were screened using polymerase chain reaction (PCR), and inhibition against six pathogens—Bacillus cereus, Yersinia enterocolitica, Salmonella enterica, Listeria monocytogenes, Escherichia coli, and Staphylococcus aureus—was assessed through reciprocal dilution assays. The inhibition zone sizes ranged from 5 mm to 20 mm, with the most significant inhibition observed in B. cereus and Y. enterocolitica. Statistical analysis revealed that the Random Forest model achieved an accuracy of 85% and an R² of 0.62 in predicting antimicrobial activity, indicating a robust predictive capability. Random Forest modeling was utilized to establish a connection between gene presence-absence data and antimicrobial performance. Strains exhibiting broad pln repertoires (e.g., isolates 405 and 465) demonstrated strong, multi-target inhibition, while some with limited gene sets, such as isolate 954 containing only plnG, displayed potent but highly specific activity. Across various pathogens, plnB consistently emerged as the most significant predictor, with plnD, plnC, and plnG contributing in a pathogen-specific manner. The models accounted for 50–65% of the variance in activity (R² = 0.50–0.65), indicating a reliable predictive capability. These findings suggest that the genetic composition of the plantaricin cluster is a crucial determinant of antimicrobial behavior in L. plantarum. The integration of targeted genomics with machine learning offers a practical approach for selecting strains optimized for food preservation and probiotic applications, with the potential to reduce reliance on chemical preservatives.