发布者:抗性基因网 时间:2020-04-22 浏览量:969
摘要
背景:
抗菌素耐药性(AMR)是一个世界性的公共卫生问题。目前广泛存在的AMR污染对准确划分源库关系提出了很大的挑战,而点源和非点源以及复杂环境条件下的内源和外源交叉反应进一步混淆了这一问题。由于在定量框架内识别源库关系的能力不足,传统的基于抗药性基因(ARG)特征的源跟踪方法很难成为一种实用的解决方案。
结果:
本文将广谱ARG谱分析与机器学习分类源跟踪相结合,提出了一种解决高通量测序时代问题的新方法。它在广泛应用方面的潜力首先得到了656个全球范围的样本的验证,这些样本涵盖了不同的环境类型(如人/动物肠道、废水、土壤、海洋)和广阔的地理区域(如中国、美国、欧洲、秘鲁)。利用具有代表性的源比例人工构形,对其在源预测中的潜力和局限性以及参数调整的效果进行了严格评价。将源跟踪技术应用于区域分析时,在污水处理厂的进水和出水两种来源成分明显不同的样品类型中,利用ARG剖面进行分析,取得了良好的效果。人类干扰梯度的两组环境亚基因组数据进一步支持了其实际应用的潜力。为了补充基于剖面的污染源跟踪在连续梯度污染识别中的作用,本研究确定了几个跨生态类型的通用和专家指标ARGs。
结论:
我们首次证明,所开发的源跟踪平台与适当的实验设计和有效的亚基因组分析工具相结合,将对评估AMR污染具有重要意义。根据预测的污染源贡献状况,可以对不同污染源在ARG传播中的风险进行排序,从而为确定减缓ARG传播的优先次序和设计有效的控制策略铺平道路。
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.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5966912/