Impact of fluoroquinolone and heavy metal pollution on antibiotic resistance maintenance in aquatic ecosystems
Bibliographic record
Abstract
BACKGROUND: Freshwater pollution with compounds used during anthropogenic activities could be a major driver of antibiotic resistance emergence and dissemination in environmental settings. Fluoroquinolones and heavy metals are two widely used aquatic pollutants that show a high stability in the environment and have well-known effects on antibiotic resistance selection. However, the impact of these compounds on antibiotic resistance maintenance in aquatic ecosystems remains unknown. In this study, we used a microcosm approach to determine the persistence of two fluoroquinolones (ciprofloxacin, ofloxacin) and two heavy metals (copper and zinc) in the Rhône river over 27 days. In addition, we established links between antibiotic and metal pollution, alone and in combination, and the composition of freshwater bacterial communities, the selection of specific members and the selection and maintenance of antibiotic and metal resistance genes (ARGs and MRGs) using a metagenomics approach. RESULTS: Whereas ofloxacin was detected at higher levels in freshwater after 27 days, copper had the strongest influence on bacterial communities and antibiotic and metal resistance gene selection. In addition, heavy metal exposure selected for some ARG-harboring bacteria that contained MRGs. Our research shows a heavy metal-driven transient co-selection for fluoroquinolone resistance in an aquatic ecosystem that could be largely explained by the short-term selection of Pseudomonas subpopulations harboring both fluoroquinolone efflux pumps and copper resistance genes. CONCLUSION: This research highlights the complexity and compound-specificity of dose-response relationships in freshwater ecosystems and provides new insights into the medium-term community structure modifications induced by overall sub-inhibitory levels of antibiotic and heavy metal pollution that may lead to the selection and maintenance of antibiotic resistance in low-impacted ecosystems exposed to multiple pollutants.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".