伍娇¹,
饶大伟¹,
严涛²,
王爽¹,
陈鹏¹,
张永奎¹,
杨坤¹
1. 四川大学化学工程学院, 成都 610065;
2. 夏威夷马诺大学土木与环境工程系, 美国夏威夷 96822

作者简介: 伍娇(1992-),女,硕士研究生,研究方向为生物制药技术,E-mail:18902099568@163.com.

基金项目: 国家自然科学基金(21677104)


中图分类号: X171.5

Minimum Inhibitory Concentrations for Assessment of Environmental Escherichia coli Antibiotic Resistance

Wu Jiao¹,
Rao Dawei¹,
Yan Tao²,
Wang Shuang¹,
Chen Peng¹,
Zhang Yongkui¹,
Yang Kun¹
1. School of Chemical Engineering, Sichuan University, Chengdu 610065, China;
2. Department of Civil and Environmental Engineering, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA

CLC number: X171.5

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摘要:2015年世界卫生组织将抗生素的滥用列为21世纪最大的挑战之一，全球范围内抗生素抗药性的散播已严重威胁人类的健康。如何检测环境中的细菌抗药性，并有效评估抗药性感染的风险，是环境微生物研究的一项重要课题。本文通过改进的培养基微量稀释(broth micro-dilution)法，确定了2个地区(中国成都和美国夏威夷)不同环境来源(天然水系和市政污水)的大肠杆菌的抗生素最小抑制浓度(minimum inhibitory concentrations, MICs)。计算所得的MIC分位数(MIC50和MIC90)、菌体抗性百分比及多抗药性指数(multiple antibiotic resistance indexes, MARIs)显示两地不同环境区划的抗生素抗性存在明显的差异。天然水系(成都锦江)中的抗生素抗性是随时间可变的，与当地的降雨事件相关。环境菌株的抗药性模式通过聚类和非度量多维测度(non-metric multidimensional scaling，NMDS)进行分析。广谱β-内酰胺酶基因筛查显示出抗性基因与抗性表型之间的正相关性。结合现有的两地抗生素的使用数据讨论了两地环境抗生素抗性与当地人类活动及抗生素的使用实践之间的紧密联系。采集环境菌株抗生素MIC数据的实验及数据分析方法实现了环境抗药性的跨时空对比，为规范抗生素的区域性使用提供了指导作用。
关键词: 抗生素/
环境抗生素抗性/
抗生素最小抑制浓度/
统计学分析/
非度量多维测度

Abstract:The bacterial antibiotic resistance induced by abuse of antibiotics has been listed as one of the major challenges in twenty-first Century in WHO report of 2015, the global dissemination of antibiotic resistance has been a serious threat to human health. Bacterial antibiotic resistance has become a pressing environmental problem. It has become an important task for environmental microbiologists to assess environmental bacterial antibiotic resistance (AR), and evaluate the risk of drug-resistant bacterial infection. The minimum inhibitory concentrations of common antibiotics against Escherichia coli (E. coli) isolates that were obtained from different environmental sources (natural waterway or municipal sewage) at two geographically distant locations (Chengdu, China and Hawaii, USA) were determined with a modified broth micro-dilution method. Calculated MIC quantiles (MIC50 and MIC90), percentages of resistance and the multiple antibiotic resistance indexes (MARIs) showed distinct bacterial AR among different environmental compartments at two regions. The AR in a natural waterway (Jin River, Chengdu) was time-variable, which was correlated with local rainfall events. The AR patterns of the environmental bacteria were analyzed via cluster and non-metric multidimensional scaling (NMDS) analysis. Brief survey on extended-spectrum β-lactamases (ESBLs) genes indicated the positive correlation between AR genes and resistant phenotypes. The close correlation of the environmental AR with local human activities and antibiotic practices was discussed incorporating into the available information of antibiotic usage at two regions. The experimental method for determining antibiotic MICs of environmental bacteria and the data analysis routine herein allows inter-regional comparison and dynamic tracking of environmental AR, which will give guidance on regional antibiotic practices.
Key words:antibiotics/
environmental antibiotic resistance/
antibiotic minimum inhibitory concentration/
statistic analysis/
NMDS.