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Tracking antibiotic resistance gene pollution from different sources using machine-learning classification
- Source :
- Microbiome, Vol 6, Iss 1, Pp 1-12 (2018), Microbiome
- Publication Year :
- 2018
- Publisher :
- BMC, 2018.
-
Abstract
- 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. Electronic supplementary material The online version of this article (10.1186/s40168-018-0480-x) contains supplementary material, which is available to authorized users.
- Subjects :
- 0301 basic medicine
Microbiology (medical)
Pollution
China
media_common.quotation_subject
010501 environmental sciences
Biology
Antibiotic resistance gene
Generalist and specialist species
01 natural sciences
Microbiology
lcsh:Microbial ecology
Machine Learning
03 medical and health sciences
Animals
Humans
Profiling (information science)
Two sample
Source tracking
Soil Microbiology
0105 earth and related environmental sciences
media_common
Bacteria
Sewage
Research
High-Throughput Nucleotide Sequencing
Drug Resistance, Microbial
6. Clean water
Anti-Bacterial Agents
Gastrointestinal Microbiome
Statistical classification
030104 developmental biology
13. Climate action
Metagenomics
lcsh:QR100-130
Biochemical engineering
Machine learning classification
Antibiotic resistance genes
Subjects
Details
- Language :
- English
- ISSN :
- 20492618
- Volume :
- 6
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Microbiome
- Accession number :
- edsair.doi.dedup.....683a06e131186733e0a942e5907a2265