摘要
     河流是人类接触抗生素耐药性的重要环境来源。许多因素可以改变河流中的抗生素耐药性，包括细菌群落、人类活动和环境因素。然而，在制药行业占主导地位的城市中，对城市河流（URs）和农村河流（RR）之间抗生素耐药性和风险差异的系统比较仍然很少。在本研究中，以中国石家庄市为例，比较URs和RR在抗生素耐药性和风险方面的差异。结果显示，从URs收集的水和沉积物样本中的总喹诺酮类抗生素（QNs）浓度高于从RR收集的总喹诺酮类抗生素浓度。URs中抗生素耐药性基因（ARGs）的亚型和丰度显著高于RR，大多数新出现的ARGs（包括OXA型、GES型、MCR型和tet（X））仅在URs中检测到。ARGs主要受URs中QNs和RR中社会经济因素（SE）的影响。URs和RR之间的细菌群落组成有显著差异。URs中抗生素耐药致病菌（ARPBs）和毒力因子（VFs）的丰度高于RR。其中，在URs和RR中分别检测到371种和326种病原体类型。大多数新出现的ARG与优先ARPB呈显著正相关。方差划分分析表明，在URs和RR中，SE是ARGs（80%）和微生物群落（92%）的主要驱动因素。结构方程模型表明，抗生素（QNs）和微生物群落分别是ARGs对URs和RR最直接的影响。在URs中QNs的累积阻力风险较高，但在RR中相对较低。恩诺沙星和氟氯喹在水中和沉积物中的风险最高。这项研究可以帮助我们更好地管理和控制不同河流中抗生素耐药性的风险。
Abstract
Rivers are important environmental sources of human exposure to antibiotic resistance. Many factors can change antibiotic resistance in rivers, including bacterial communities, human activities, and environmental factors. However, the systematic comparison of the differences in antibiotics resistance and risks between urban rivers (URs) and rural rivers (RRs) in a pharmaceutical industry dominated city is still rare. In this study, Shijiazhuang City (China) was selected as an example to compare the differences in antibiotics resistance and risks between URs and RRs. The results showed higher concentrations of total quinolones (QNs) antibiotics in both water and sediment samples collected from URs than those from RRs. The subtypes and abundances of antibiotic resistance genes (ARGs) in URs were significantly higher than those in RRs, and most emerging ARGs (including OXA-type, GES-type, MCR-type, and tet(X)) were only detected in URs. The ARGs were mainly influenced by QNs in URs and social-economic factors (SEs) in RRs. The composition of the bacterial community was significantly different between URs and RRs. The abundance of antibiotic-resistant pathogenic bacteria (ARPBs) and virulence factors (VFs) were higher in URs than those in RRs. Therein, 371 and 326 pathogen types were detected in URs and RRs, respectively. Most emerging ARGs showed a significantly positive correlation with priority ARPBs. Variance partitioning analysis revealed that SEs were the main driving factors of ARGs (80 %) and microbial communities (92 %) both in URs and RRs. Structural equation models indicated that antibiotics (QNs) and microbial communities were the most direct influence of ARGs in URs and RRs, respectively. The cumulative resistance risk of QNs was high in URs, but relatively low in RRs. Enrofloxacin and flumequine posed the highest risk in water and sediment, respectively. This study could help us to better manage and control the risk of antibiotic resistance in different rivers.

https://www.sciencedirect.com/science/article/abs/pii/S0048969722056297