摘要

       水生环境中抗生素抗性基因 (ARG) 的日益流行，因其对公共健康的潜在威胁而引起了相当大的关注。为了降低 ARGs 的环境风险，识别致病性耐药菌、确定 ARG 群落的驱动力并分配其来源至关重要，这仍有待探索。在这项研究中，我们开发了一个集成高通量测序 (HTS) 分析、基于空模型的方法和机器学习分类工具的框架，用于了解控制水生沉积物中 ARG 谱的环境抗性风险和生态过程，以及应用于中国的两个城市湖泊（太湖和白洋湖）。基于 HTS 的宏基因组分析揭示了两个湖泊中丰富多样的抗性组、移动组和病毒组，包括一些新兴的 ARG，如 mcr 和碳青霉烯酶类型。相对而言，白洋湖的ARGs、移动遗传元件（MGEs）和毒力因子基因的多样性显着高于太湖（p < 0.05）。宏基因组组装和分箱方法追踪了许多潜在的致病性抗生素抗性细菌，并发现约 50% 的沉积物样本中同时存在 ARGs、MGEs 和人类细菌病原体，表明湖泊存在重大的耐药性风险。多位点β多样性差异指数的比较表明，ARG多样性主要由空间周转而不是嵌套来解释，并表现出显着的距离衰减模式。使用新的基于空模型的随机性比率的结果表明，随机过程对环境中 ARG 剖面的贡献高于确定性过程，特别是对于白洋湖 (>65%)。通过利用基于零模型的统计框架对 ARG 群落进行各种生态过程的确定分析来量化群落组装，证实了这一点。即白洋湖以均质扩散（40%）为主，其次是均质选择（32%）和生态漂移（15%），而白洋湖以生态漂移（33%）和均质扩散（31%）为主. SourceTracker 分析显示，与人类污水相关的来源是环境中 ARG 的最大贡献者 (~62%)。研究结果揭示了水生环境中抗菌素耐药性的传播风险和驱动因素，这可能有助于制定有效的管理策略来控制 ARGs 的污染。

       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 ~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 (~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.

https://www.sciencedirect.com/science/article/pii/S016041202032300X