Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, 999077, China.
Microbiome. 2018 May 24;6(1):93. doi: 10.1186/s40168-018-0480-x.
Antimicrobial resistance (AMR) has been a worldwide public health concern. Current widespread AMR pollution has posed a big challenge in accurately disentangling source-sink relationship, which has been further confounded by point and non-point sources, as well as endogenous and exogenous cross-reactivity under complicated environmental conditions. Because of insufficient capability in identifying source-sink relationship within a quantitative framework, traditional antibiotic resistance gene (ARG) signatures-based source-tracking methods would hardly be a practical solution.
By combining broad-spectrum ARG profiling with machine-learning classification SourceTracker, here we present a novel way to address the question in the era of high-throughput sequencing. Its potential in extensive application was firstly validated by 656 global-scale samples covering diverse environmental types (e.g., human/animal gut, wastewater, soil, ocean) and broad geographical regions (e.g., China, USA, Europe, Peru). Its potential and limitations in source prediction as well as effect of parameter adjustment were then rigorously evaluated by artificial configurations with representative source proportions. When applying SourceTracker in region-specific analysis, excellent performance was achieved by ARG profiles in two sample types with obvious different source compositions, i.e., influent and effluent of wastewater treatment plant. Two environmental metagenomic datasets of anthropogenic interference gradient further supported its potential in practical application. To complement general-profile-based source tracking in distinguishing continuous gradient pollution, a few generalist and specialist indicator ARGs across ecotypes were identified in this study.
We demonstrated for the first time that the developed source-tracking platform when coupling with proper experiment design and efficient metagenomic analysis tools will have significant implications for assessing AMR pollution. Following predicted source contribution status, risk ranking of different sources in ARG dissemination will be possible, thereby paving the way for establishing priority in mitigating ARG spread and designing effective control strategies.
抗菌药物耐药性（AMR）已成为全球公共卫生关注的问题。目前广泛存在的 AMR 污染对准确区分源汇关系提出了巨大挑战，而在复杂环境条件下，点源和非点源以及内源性和外源性交叉反应进一步加剧了这一挑战。由于在定量框架内识别源汇关系的能力不足，传统的抗生素耐药基因（ARG）标记物溯源方法几乎不可能成为一种实用的解决方案。
通过将广谱 ARG 分析与基于机器学习的分类源追踪器 SourceTracker 相结合，我们提出了一种在高通量测序时代解决这一问题的新方法。该方法的广泛应用潜力首先通过涵盖多种环境类型（如人类/动物肠道、废水、土壤、海洋）和广泛地理区域（如中国、美国、欧洲、秘鲁）的 656 个全球规模样本得到了验证。然后，通过具有代表性源比例的人工配置，严格评估了其在源预测和参数调整效果方面的潜力和局限性。当在特定区域的分析中应用 SourceTracker 时，两种具有明显不同源组成的样本类型（即污水处理厂的进水和出水）中的 ARG 谱表现出优异的性能。人为干扰梯度的两个环境宏基因组数据集进一步支持了其在实际应用中的潜力。为了补充基于一般谱的源追踪在区分连续梯度污染方面的作用，本研究鉴定了几种跨越生态型的一般和专业指示 ARG。
我们首次证明，当与适当的实验设计和高效的宏基因组分析工具相结合时，开发的源追踪平台将对抗菌药物耐药性污染的评估具有重要意义。根据预测的源贡献状态，对不同源在 ARG 传播方面的风险进行排名将成为可能，从而为确定减轻 ARG 传播和制定有效控制策略的优先级铺平道路。
