背景
抗菌素耐药性（AMR）一直是全球公共卫生关注的焦点。当前广泛的AMR污染对准确地分离源-汇关系提出了巨大挑战，而点源和非点源以及复杂环境条件下的内源性和外源性交叉反应又进一步混淆了这一点。由于在定量框架内识别源库关系的能力不足，传统的基于抗生素抗药性基因（ARG）签名的源跟踪方法将很难成为现实的解决方案。

结果
通过将广谱ARG分析与机器学习分类SourceTracker结合起来，这里我们提出了一种新颖的方法来解决高通量测序时代的问题。它的广泛应用潜力首先被656个全球规模的样本所验证，这些样本涵盖了各种环境类型（例如人/动物肠，废水，土壤，海洋）和广阔的地理区域（例如中国，美国，欧洲，秘鲁）。然后通过具有代表性源比例的人工配置，严格评估其在源预测中的潜力和局限性以及参数调整的效果。当将SourceTracker应用于特定区域分析时，通过ARG配置文件在两种样品类型中具有优异的性能，这些样品类型具有明显不同的来源组成，即废水处理厂的进水和出水。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。为了补充基于一般资料的源跟踪以区分连续梯度污染，本研究确定了一些跨生态型的通才和专业指标ARG。

结论
我们首次证明，开发的源跟踪平台与适当的实验设计和有效的宏基因组分析工具结合使用，将对评估AMR污染产生重大影响。根据预测的源贡献状态，将有可能在ARG传播中对不同源进行风险排名，从而为在减轻ARG传播和设计有效控制策略方面确立优先级铺平道路。

Background
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.

Results
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.

Conclusion
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.

https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-018-0480-x