Environmental risk characterization and ecological process determination of bacterial antibiotic resistome in lake sediments

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 < 0.05). The metagenomic assembly and binning approaches tracked a number of potential pathogenic antibiotic resistant bacteria and found the co-occurrence of ARGs, MGEs and human bacterial pathogens in similar to 50% of the sediment samples, indicating a substantial resistome risk in the lakes. Comparison of multiple-site beta-diversity dissimilarity indexes suggested the ARG diversity was mainly explained by the spatial turnover rather than nestedness and exhibited significant distance-decay pattern. The results of using a novel null-model-based stochasticity ratio showed the stochastic processes made a higher contribution than the deterministic processes on the ARG profile in the environment, especially for Lake Baiyang (>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 (similar to 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.

Automated summary

This study used high-throughput sequencing and machine learning tools to reveal abundant and diverse antibiotic resistance genes in the sediment of Lake Tai and Lake Baiyang in China, identified potential pathogenic antibiotic resistant bacteria and co-occurrence of resistance pathways, confirming a significant resistome risk in the lakes.