Multiplexed Amplicon Sequencing Reveals High Sequence Diversity of Antibiotic Resistance Genes in Québec Sewers
Bibliographic record
Abstract
Abstract The United Nations Environment Assembly (UNEA-3) have recognised the importance of the environment in the development, spread and transmission of antimicrobial resistance (AMR) to humans and animals. Such recognition calls for wider surveillance of antimicrobial resistance genes (ARG) in wastewater and other environmental reservoirs. For ARG surveillance to be valuable to regulators, it must enable source tracking and risk assessment. Adequate surveillance also requires the processing of a large number of samples at a relatively low cost, and a low detection limit to allow quantification of the riskiest ARGs. However, current methods for tracking ARGs have various limitations. The current study presents a multiplexed targeted amplicon sequencing approach for the detection of sequence variants of ARGs in environmental samples. To demonstrate the application of this technique, wastewater samples collected from the inlet to 16 treatment plants located along a 440-km transect of the St-Lawrence river in the province of Quebec (Canada) were analysed. Among the ARGs examined, between 3 and 45 nucleic acid sequence variants were detected demonstrating the high sequence diversity that occurs within genes originating from a single sample type and the information that is missed using traditional techniques. Using the PLSDB and Comprehensive Antibiotic Resistance Database (CARD), the risk of ARG sequence variants was inferred based upon their reported mobility and detection in pathogens. Results suggest that sequence variants within a single ARG class present different risks to public health. In the future, targeted amplicon sequencing could be a valuable tool in environmental studies for both risk assessment purposes and in AMR source tracking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".