废水厂是抗生素耐药菌(ARB)和抗生素耐药基因(ARGs)的巨大储存地.为调查医药化工废水处理厂中的ARB和ARGs,采用了宏基因组技术对医药化工废水中的活性污泥进行取样分析.结果显示,医药化工废水厂微生物组成主要是细菌类,主要细菌门是Proteobacteria,主要属是Hyphomicrobium,主要种是Hyphomicrobium zavarzinii.共检测到74类ARGs,最主要的类型是sav1866、dfr E和mfd.网络分析揭示了ARGs与微分类单元之间的共存模式,即ARGs与废水厂中属级的微生物分类群高度相关.抗生素特异的外排泵是该微生物群落主要的抗生素耐药机制,并且外排泵中耐药结节化细胞分化家族(RND)外排泵占主要部分.该微生物群落最主要的功能通路是代谢相关,并存在许多与人类疾病相关的基因,其中主要是细菌感染性疾病.结果表明,医药化工废水厂蕴藏着丰富的ARB和ARGs,ARGs的累积会增加潜在环境风险,需要加强对医药化工废水厂中ARB和ARGs的监控,并且ARB和ARGs的分析研究对于选择深度处理技术来有效去除ARB和ARGs具有重要的指导意义.
Wastewater treatment plants hold a vast pool of antibiotic resistant bacteria( ARB) and antibiotic resistance genes( ARGs).The aim of this study is to analyze the ARB and ARGs in a pharmaceutical and chemical wastewater treatment plant using a metagenomic technique. The results of taxonomic annotation revealed that bacteria were the predominant domain. The most abundant phyla and genus was Proteobacteria and Hyphomicrobium,respectively. A total of 74 categories of ARGs were predicted using CARD with the most dominant types being sav1866,dfr E,and mfd. Furthermore,a network analysis was conducted to investigate the cooccurrence patterns between ARGs and microbial taxa. ARGs were found to be highly connected to microbial taxa at the genus level.With respect to the antibiotic resistance mechanisms,antibiotic-specific efflux pumps appeared to be the most common mechanisms.Among these,resistance-nodulation-cell division( RND) was the major type. The most important functional pathway of this microbial community was metabolic correlation. Interestingly,there were many genes related to human diseases,among which bacterial infectious diseases were the main ones. On the one hand,these data further confirmed that pharmaceutical and chemical wastewater treatment plants are rich in ARB and ARGs. The accumulation of ARGs increases the potential environmental risks,and hence it is necessary to strengthen the active monitoring of ARB and ARGs in pharmaceutical and chemical wastewater treatment plants. On the other hand,research on ARB and ARGs offers important information for the selection of deep processing technology to effectively remove ARB and ARGs.