| 典型城市不同河流沉积物中抗生素抗性基因的时空分布特征:以石家庄为例 |
| 摘要点击 1276 全文点击 237 投稿时间：2023-06-11 修订日期：2023-08-23 |
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| 中文关键词 抗生素抗性基因(ARGs) 时空分布 宏基因组 城市河流 农村河流 社会经济因子 |
| 英文关键词 antibiotic resistance gene (ARGs) spatial-temporal distribution metagenome urban rivers rural rivers social-economical factor |
| 作者 | 单位 | E-mail | | 赵波 | 河北科技大学环境科学与工程学院, 石家庄 050000 | 18843436029@163.com | | 宋圆梦 | 河北科技大学环境科学与工程学院, 石家庄 050000 | | | 卢梦淇 | 河北科技大学环境科学与工程学院, 石家庄 050000 | | | 白寅虎 | 河北省水利规划设计研究院有限公司, 石家庄 050021 | | | 陈昊达 | 河北科技大学环境科学与工程学院, 石家庄 050000 | | | 高赛 | 河北科技大学环境科学与工程学院, 石家庄 050000 | | | 王琳静 | 河北科技大学环境科学与工程学院, 石家庄 050000 | | | 张璐璐 | 河北科技大学环境科学与工程学院, 石家庄 050000
河北省污染防治生物技术实验室, 石家庄 050000 | zhanglulu19850703@163.com | | 崔建升 | 河北科技大学环境科学与工程学院, 石家庄 050000
河北省污染防治生物技术实验室, 石家庄 050000 | |
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| 中文摘要 |
| 河流是抗生素抗性基因（ARGs）的重要储库.然而,目前有关河流中ARGs的研究多集中在其时空分布,而较少对同一区域城市和农村河流中ARGs的种类和丰度进行比较.鉴于此,以石家庄市不同河流为例,分别在2020年12月和2021年4月,选取了2条农村河流和3条城市河流,布设了15个采样点,采集了各个样点的沉积物,运用宏基因组测序技术分析了各样点沉积物中ARGs的种类和丰度,比较了城市和农村河流沉积物中ARGs的时空差异.结果表明:①在城市和农村河流中分别检出162种（4 776 ±4 452,丰度,下同）和79种ARGs（1 043 ±632）,ARGs种类和丰度均呈城市 > 农村的趋势；②在城市河流中磺胺类（SAs,27 %）、氨基糖苷类（AGs,26 %）和多药类（MDs,15 %） ARGs的相对丰度最高；而在农村河流中MDs类ARGs的相对丰度最高（65 %）,且城市河流中ARGs的复杂程度高于农村河流.③城市河流中SAs、AGs、MDs、四环素类（TCs）、利胆醇类（PNs）、大环内酯类（MLS）、β-内酰胺类（β-lactams）和二氨基嘧啶类（DAPs）ARGs间呈显著正相关（P < 0.01）,而糖肽类（GPs）ARGs与各类ARGs呈显著负相关（P < 0.05和P < 0.01）；而在农村河流中MDs和SAs类ARGs呈显著正相关（P < 0.05）,氨基香豆素类（ACs）ARGs与肽类（PTs）、利福霉素类（RMs）和磷霉素类（FMs）ARGs呈显著负相关（P < 0.05和P < 0.01）；④在时间分布上,城市河流12月和4月分别检出162种（4 776 ±4 452）和148种（5 673 ±5 626）ARGs；而农村河流12月和4月分别检出79种（1 043 ±632）和46种（467 ±183）ARGs；⑤RDA分析结果表明,城市和农村河流中的ARGs均呈时间分布差异；相关分析表明城市河流主要与工业企业数量显著相关,而农村河流则主要与牧业产值显著相关.总体而言,通过比较城市和农村河流中ARGs的时空异质性,识别其主要社会影响因子,可为后续河流中ARGs风险管控提供数据支撑. |
| 英文摘要 |
| Rivers are important reservoirs of antibiotic resistance genes (ARGs). However, most current studies have focused on the temporal and spatial distribution, and data on the differences in the species and abundance of ARGs between urban and rural rivers is still lacking for certain areas. In view of this, two rural rivers and three urban rivers were selected in Shijiazhuang City. In both December 2020 and April 2021, sediments were collected at 15 sampling sites. Metagenomic sequencing technology was used to compare the differences in temporal-spatial variation for ARGs in sediments. The results showed that:① 162 and 79 ARGs were detected in urban (4 776 ±4 452) and rural rivers (1 043 ±632), respectively. The abundance and species of ARGs in urban rivers were higher than those in rural rivers. ② The relative abundances of sulfonamide (SAs,27 %), aminoglycoside (AGs,26 %), and multidrug (MDs,15 %) ARGs had the highest abundance in urban rivers, whereas the relative abundance of MDs ARGs was highest in rural rivers (65 %). On the whole, the complexity of ARGs in urban rivers was higher than that in rural rivers. ③ There was a significant positive correlation between SAs, AGs, MDs, tetracycline, phenicol, macrolides-lincosamids-streptogramins (MLS), β-lactams, and diaminopyrimidine ARGs in urban rivers (P < 0.01); however, there was a significant negative correlation between glycopeptide ARGs and all types of ARGs (P < 0.05 and P < 0.01). There was a significant positive correlation between MDs and SAs ARGs in rural rivers (P < 0.05), but there was a significant negative correlation between amino aminocoumarin, peptide, rifamycin, and fosfomycin ARGs (P < 0.05 and P < 0.01). ④ For the temporal variation in urban rivers, 162 ARGs (4 776 ±4 452) and 148 ARGs (5 673 ±5 626) were detected in December and April, respectively. For the temporal variation in rural rivers, 79 species (1 043 ±632) and 46 species (467 ±183) were detected in December and April, respectively. ⑤ RDA analysis results showed that the spatial-temporal distributions of ARGs in urban and rural rivers were different. Correlation analysis showed that the ARGs in urban rivers were significantly correlated with the number of industrial enterprises, whereas the ARGs in rural rivers were significantly correlated with the output value of animal husbandry. In general, this study identified the main influencing factors for ARGs in different rivers and provided data support for ARGs risk management in different rivers. |
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