Tracking antibiotic resistance gene pollution from different sources using machine-learning classification

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-reac...

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Bibliographic Details
Published in:	Microbiome 2018-05, Vol.6 (1), p.93-93, Article 93
Main Authors:	Li, Li-Guan, Yin, Xiaole, Zhang, Tong
Format:	Article
Language:	eng
Subjects:	Animals Anti-Bacterial Agents - pharmacology Antibiotic resistance Antibiotic resistance gene Antibiotics Antimicrobial resistance Bacteria - drug effects Bacteria - genetics China Classification Cross-reactivity Drug resistance Drug resistance in microorganisms Drug Resistance, Microbial - genetics Ecotypes Environmental conditions Gastrointestinal Microbiome - genetics Genes Genomics High-Throughput Nucleotide Sequencing Humans Learning algorithms Machine Learning Machine learning classification Metagenomics Next-generation sequencing Plasmids Pollution Public health Purification Rankings Sewage Sewage - microbiology Soil Microbiology Source tracking Wastewater Wastewater treatment Water treatment
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Summary:	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.
ISSN:	2049-2618 2049-2618