Anthropogenic contributions to antibiotic resistance gene pollution in household drinking water revealed by machine-learning-based source-tracking

污染 基因组 四环素 流动遗传元素 废水 环境科学 水质 污染物 源跟踪 抗生素耐药性 生物 水污染 环境化学 生态学 环境工程 基因 抗生素 微生物学 化学 遗传学 基因组 万维网 计算机科学
作者
Chen Wang,Huiying Yang,Huafeng Liu,Xuxiang Zhang,Liping Ma
出处
期刊:Water Research [Elsevier BV]
卷期号:246: 120682-120682 被引量:47
标识
DOI:10.1016/j.watres.2023.120682
摘要

Although the presence of antibiotic resistance genes (ARGs) in drinking water and their potential horizontal gene transfer to pathogenic microbes are known to pose a threat to human health, their pollution levels and potential anthropogenic sources are poorly understood. In this study, broad-spectrum ARG profiling combined with machine-learning-based source classification SourceTracker was performed to investigate the pollution sources of ARGs in household drinking water collected from 95 households in 47 cities of eight countries/regions. In total, 451 ARG subtypes belonging to 19 ARG types were detected with total abundance in individual samples ranging from 1.4 × 10-4 to 1.5 × 10° copies per cell. Source tracking analysis revealed that many ARGs were highly contributed by anthropogenic sources (37.1%), mainly wastewater treatment plants. The regions with the highest detected ARG contribution from wastewater (∼84.3%) used recycled water as drinking water, indicating the need for better ARG control strategies to ensure safe water quality in these regions. Among ARG types, sulfonamide, rifamycin and tetracycline resistance genes were mostly anthropogenic in origin. The contributions of anthropogenic sources to the 20 core ARGs detected in all of the studied countries/regions varied from 36.6% to 84.1%. Moreover, the anthropogenic contribution of 17 potential mobile ARGs identified in drinking water was significantly higher than other ARGs, and metagenomic assembly revealed that these mobile ARGs were carried by diverse potential pathogens. These results indicate that human activities have exacerbated the constant input and transmission of ARGs in drinking water. Our further risk classification framework revealed three ARGs (sul1, sul2 and aadA) that pose the highest risk to public health given their high prevalence, anthropogenic sources and mobility, facilitating accurate monitoring and control of anthropogenic pollution in drinking water.
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