Abstract
Hospital wastewater is a critical source of antimicrobial resistance (AMR), which facilitates the proliferation and spread of clinically significant antimicrobial resistance genes (ARGs) and pathogenic bacteria. This study utilized metagenomic approaches, including advanced binning techniques, such as MetaBAT2, MaxBin2, and CONCOCT, which offer significant improvements in accuracy and completeness over traditional binning methods. These methods were used to comprehensively assess the dynamics and composition of resistomes and mobilomes in untreated wastewater samples taken from two general hospitals and one cancer hospital. This study revealed a diverse bacterial landscape, largely consisting of Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, with notable variations in microbial composition among hospitals. Analysis of the top 15 genera showed unique microbial pattern distribution in each hospital: Aeromonas was predominant in 1stHWTS (49.39 %), Acidovorax in the CAHWTS at 16.85 %, and Escherichia and Bacteroides in the 2ndHWTS at 11.44 % and 11.33 %, respectively. A total of 114 pathogenic bacteria were identified, with drug-resistant Aeromonas caviae and Escherichia coli being the most prevalent. The study identified 34 types and 1660 subtypes of ARGs, including important last-resort antibiotic resistance genes (LARGs), such as blaNDM, mcr, and tet(X). Using metagenomic binning, this study uncovered distinct patterns of host-resistance associations, particularly with Proteobacteria and Firmicutes. Network analysis highlighted the complex interactions among ARGs, mobile genetic elements (MGEs), and bacterial species, all contributing to the dissemination of AMR. These findings emphasize the intricate nature of AMR in hospital wastewater and the influence of hospital-specific factors on microbial resistance patterns. This study provides support for implementing integrated management strategies, including robust surveillance, advanced wastewater treatment, and strict antibiotic stewardship, to control the dissemination of AMR. Understanding the interplay among bacterial communities, ARGs, and MGEs is important for developing effective public health measures against AMR.
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