Identifying ARG-carrying bacteriophages in a lake replenished by reclaimed water using deep learning techniques

基因组 生物 流动遗传元素 质粒 生物信息学 抗生素耐药性 噬菌体 温带气候 水平基因转移 基因组 抵抗性 微生物学 大肠杆菌 抗生素 基因 生态学 遗传学
作者
Donglin Wang,Jiayu Shang,Hui Lin,Jinsong Liang,Chenchen Wang,Yanni Sun,Yaohui Bai,Jiuhui Qu
出处
期刊:Water Research [Elsevier BV]
卷期号:248: 120859-120859 被引量:7
标识
DOI:10.1016/j.watres.2023.120859
摘要

As important mobile genetic elements, phages support the spread of antibiotic resistance genes (ARGs). Previous analyses of metaviromes or metagenome-assembled genomes (MAGs) failed to assess the extent of ARGs transferred by phages, particularly in the generation of antibiotic pathogens. Therefore, we have developed a bioinformatic pipeline that utilizes deep learning techniques to identify ARG-carrying phages and predict their hosts, with a special focus on pathogens. Using this method, we discovered that the predominant types of ARGs carried by temperate phages in a typical landscape lake, which is fully replenished by reclaimed water, were related to multidrug resistance and β-lactam antibiotics. MAGs containing virulent factors (VFs) were predicted to serve as hosts for these ARG-carrying phages, which suggests that the phages may have the potential to transfer ARGs. In silico analysis showed a significant positive correlation between temperate phages and host pathogens (R = 0.503, p < 0.001), which was later confirmed by qPCR. Interestingly, these MAGs were found to be more abundant than those containing both ARGs and VFs, especially in December and March. Seasonal variations were observed in the abundance of phages harboring ARGs (from 5.62% to 21.02%) and chromosomes harboring ARGs (from 18.01% to 30.94%). In contrast, the abundance of plasmids harboring ARGs remained unchanged. In summary, this study leverages deep learning to analyze phage-transferred ARGs and demonstrates an alternative method to track the production of potential antibiotic-resistant pathogens by metagenomics that can be extended to microbiological risk assessment.
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