Metagenomic assembly insight into the antibiotic resistance genes and antibiotic resistant bacteria in packaged drinking water system

The widespread use of antibiotics has led to an increasing spread of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB). However, the distribution and composition of ARGs and ARB in packaged drinking water system (PDWS) is unknown. In this study, metagenomics was used to characterize the occurrence and abundance of ARGs and ARB in a PDWS. Meanwhile, antimicrobial susceptibility testing was used to characterize the antibiotic resistance of bacteria. The results indicated that a total of 1136 ARG subtypes belonging to 27 ARG types were detected. Among them, multidrug-resistant antibiotic resistance genes, tetracycline antibiotic resistance genes, and peptide antibiotic resistance genes were the three types of ARGs with the highest levels in PDWS. Further analysis shows that the dominant ARG subtypes were macB, evgS, TxR, bcrA, and tetA (58). Bin analysis indicated that binMF.85 contained the largest number of ARG subtypes (n = 43), including multidrug (n = 32), aminoglycoside antibiotic (n = 3), peptide antibiotic (n = 2), phosphonic acid antibiotic (n = 2), the fluoroquinolone antibiotic (n = 1), aminocoumarin antibiotic (n = 2), and phenicol antibiotic (n = 1), respectively. A total of fifty-eight strains were isolated from PDWS, and Sphingomonas (13.79 %) and Methylobacterium (13.79 %) were the most abundant genera in all isolates. Furthermore, some opportunistic pathogenic bacteria such as Bacillus cereus and Bacillus immobilis were also detected. Antimicrobial susceptibility testing showed that 72 % of the representative isolates belonged to Multi-drug resistant bacteria (MDR). These results provide insights into the profiles of ARGs and ARB in PDWS. Our study also highlights the effective disinfection technology need to be introduce to PDWS in order to ensure the packaged drinking water safety. To the best of our knowledge, this is the first study that monitors the presence and distribution of antibiotic resistance in PDWS using metagenomics.