摘要：The increasing prevalence of antibiotic resistance genes (ARGs) in aquatic environments has attracted considerable concerns due to their potential threat to public health. For reducing environmental risk of ARGs, it is crucial to identify the pathogenic resistant bacteria, determine the driving forces governing the ARG community and apportion their sources, which is yet remained to explore. In this study, we developed a framework integrating high-throughput sequencing (HTS) analyses, null-model-based methods and machine-learning classification tool for understanding the environmental resistome risk and the ecological processes that control the ARG profile in aquatic sediments, and applied to two urban lakes (Lake Tai and Lake Baiyang) in China. The HTS-based metagenomic analyses revealed abundant and diverse resistome, mobilome and virulome in the two lakes, including some emerging ARGs such as mcr and carbapenemases types. Relatively, the diversities for ARGs, mobile genetic elements (MGEs) and virulence factor genes in Lake Baiyang were significantly higher than those in Lake Tai ( p 65%). This was confirmed by the determination analyses of various ecological processes on ARG community by utilizing the null-model-based statistical framework for quantifying community assembly. That is, homogenizing dispersal (40%) dominated in Lake Baiyang, followed by homogeneous selection (32%) and ecological drift (15%), while ecological drift (33%) and homogenizing dispersal (31%) were the dominators in Lake Baiyang. SourceTracker analysis showed human sewage-associated sources were the largest contributor (~62%) of ARGs in the environment. The findings shed light on the dissemination risk and driver dynamics of antimicrobial resistance in the aquatic environment, which may help to make effective management strategies for controlling pollution of ARGs.
关键词：Antibiotic resistomes ; Environmental risk ; Ecological process ; Microbial source tracking ; Metagenomic assembly and binning analyses
