Integrated qPCR and Shotgun Metagenomics for Surveillance of Antimicrobial Resistance in Municipal Wastewater from Central Italy.

Background/Objectives: Wastewater-based epidemiology (WBE) has emerged as a valuable One Health approach for monitoring antimicrobial resistance (AMR) at the population level. Although quantitative PCR (qPCR) and shotgun (SG) metagenomics are widely used for wastewater surveillance, studies integrating these complementary approaches remain limited. This study aimed to investigate the occurrence, seasonal dynamics, and diversity of antimicrobial resistance genes (ARGs) in municipal wastewater from Central Italy by combining targeted qPCR and SG metagenomic sequencing. Methods: Influent wastewater samples were collected monthly from eight municipal wastewater treatment plants in Central Italy between April 2025 and March 2026. Clinically relevant antimicrobial resistance genes were quantified by quantitative real-time PCR, while SG metagenomic sequencing was used to characterize resistome composition, resistance gene families, and ARG sequence diversity using bioinformatic pipelines. Results: All investigated ARGs were detected in every sample. Significant seasonal variation was observed for all investigated markers, including qnrS, blaKPC, blaCTX-M and intI1. Metagenomic analysis revealed broadly similar resistome profiles across sampling sites and time points, dominated by resistance genes to macrolide-lincosamide-streptogramin, aminoglycosides, β-lactams, and fluoroquinolones. High sequence diversity was observed within the dominant ARG families, highlighting the complementary value of SG metagenomics for comprehensive resistome characterization. Conclusions: The integration of targeted qPCR and SG metagenomics provided a comprehensive characterization of antimicrobial resistance in municipal wastewater. While qPCR enabled sensitive quantification of clinically relevant ARGs and revealed seasonal trends, metagenomics expanded resistome characterization by identifying dominant resistance classes, gene families, and sequence variants. These findings support the implementation of integrated molecular approaches for routine wastewater-based AMR surveillance within a One Health framework.