An integrated risk profiling and analysis of antimicrobial resistance genes by shotgun metagenomics and qPCR in retail food items

Antibiotic resistance genes (ARGs) pose a major public health concern due to the transmission of foodborne pathogens. This study used shallow whole-metagenome shotgun sequencing (sWMS) and qPCR to conduct ARG-based risk profiling of retail cabbage, lettuce, chicken legs, and deli meats collected from low- and high-socioeconomic status (LSES, HSES) neighborhoods in Memphis, USA. The sWMS using the SARG database showed that multidrug resistance was the dominant ARG class (1.119–1.720 copies/16S rRNA). Chicken legs from HSES stores exhibited the highest ARG abundance (3.646 copies/16S rRNA), followed by deli meats from HSES stores (2.916 copies/16S rRNA). ARG richness and evenness did not differ significantly among sample groups ( p > 0.05). Emerging ARG analysis identified deli meats as high-risk regardless of socioeconomic status, while cabbage and chicken legs from HSES stores showed added risk due to mobile ARGs linked to pathogenic bacteria. Co-association network analysis revealed strong associations between ARGs and bacterial genera including Pseudomonas , Enterobacter , Enterococcus , Serratia , Pantoea , and Weissella . A locally constructed ARG database built using PCR-validated primers improved prediction accuracy compared with SARG and was verified using qPCR. These findings underscore the value of integrating multiple molecular approaches to better characterize ARGs and guide targeted interventions aimed at reducing antibiotic resistance risks across the food supply chain. • Retail foods from different store types exhibited a high abundance of ARGs. • All samples contained potential pathogen-associated ARGs. • Deli meats showed the highest load of emerging ARGs irrespective of store types. • Cabbage and chicken legs carried highest load of ARGs from risk group I. • Network analysis showed ARGs positively correlated with potential pathogens.