Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
Water Res. 2024 Jan 1;248:120859. doi: 10.1016/j.watres.2023.120859. Epub 2023 Nov 11.
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.
作为重要的移动遗传元件，噬菌体支持抗生素耐药基因 (ARG) 的传播。以前的 metaviromes 或 metagenome-assembled genomes (MAGs) 分析未能评估噬菌体转移的 ARG 的程度，特别是在抗生素病原体的产生方面。因此，我们开发了一种生物信息学管道，利用深度学习技术来识别携带 ARG 的噬菌体并预测其宿主，特别关注病原体。使用这种方法，我们发现，在一个典型的景观湖中，完全由再生水补充的温和噬菌体携带的主要类型的 ARG 与多药耐药和β-内酰胺类抗生素有关。预测含有毒力因子 (VF) 的 MAG 可以作为这些携带 ARG 的噬菌体的宿主，这表明噬菌体可能有潜力转移 ARG。计算机分析显示温和噬菌体与宿主病原体之间存在显著的正相关关系 (R = 0.503，p < 0.001)，随后通过 qPCR 得到了证实。有趣的是，与同时含有 ARG 和 VF 的 MAG 相比，这些 MAG 的丰度更高，特别是在 12 月和 3 月。携带 ARG 的噬菌体 (从 5.62%到 21.02%) 和携带 ARG 的染色体 (从 18.01%到 30.94%) 的丰度都出现了季节性变化。相比之下，携带 ARG 的质粒的丰度保持不变。总之，本研究利用深度学习分析噬菌体转移的 ARG，并展示了一种通过宏基因组学跟踪潜在抗生素耐药病原体产生的替代方法，该方法可以扩展到微生物风险评估。
