Evaluating the Potential Antibiotic Resistance Status in Environment Based on the Trait of Microbial Community

The overuse of antibiotics has promoted the propagation and dissemination of antibiotic resistance genes (ARGs) in environment. Due to the dense human population and intensive activities in coastal areas, the health risk of ARGs in coastal environment is becoming a severe problem. To date, there still lacks of a quantitative method to assess properly the gross antibiotic resistance at microbial community level. Here, we collected sediment samples from Hangzhou Bay (HB), Taizhou Bay (TB), and Xiangshan Bay (XB) of the East China Sea for community-level ARGs analysis. Based on the 16S rRNA genes and predictive metagenomics, we predicted the composition of intrinsic ARGs (piARGs) and some related functional groups. Firstly, a total of 40 piARG subtypes, belonging to nine drug classes and five resistance mechanisms, were obtained, among which the piARGs encoding multidrug efflux pumps were the most dominant in the three bays. Secondly, XB had higher relative abundances of piARGs and pathogens than the other two bays, which posed higher potential health risk and implied the heavier impact of long-term maricultural activities in this bay. Thirdly, the co-occurrence network analysis identified that there were more connections between piARGs and some potential pathogenic bacteria. Several piARG subtypes (e.g., tetA, aacA, aacC, and aadK) distributed widely in the microbial communities. And finally, the microbial diversity correlated negatively with the relative abundance of piARGs. Oil, salinity, and arsenic had significant effects on the variations of piARGs and potential pathogenic bacteria. The abundance-weighted average ribosomal RNA operon (rrn) copy number of microbial communities could be regarded as an indicator to evaluate the antibiotic resistance status. In conclusion, this study provides a new insight on how to evaluate antibiotic resistance status and their potential risk in environment based on a quantitative analysis of microbial communities.