Differential effects of wastewater treatment plant effluents on the antibiotic resistomes of diverse river habitats
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) are key sources of antimicrobial resistance genes (ARGs) that could influence the resistomes of microbial communities in various habitats of the receiving river ecosystem. However, it is currently unknown which habitats are most impacted and whether ARGs, like certain chemical contaminants, could be accumulated or enriched in the river ecosystem. We conducted a systematic metagenomic survey on the antibiotic resistomes of WWTP effluent, four riverine habitats (water, suspended particles, sediment, epilithic biofilm), and freshwater amphipod gut microbiomes. The impact of WWTP effluent on the downstream habitats was assessed in nine Swiss rivers. While there were significant differences in resistomes across habitats, the wastewater resistome was more similar to the resistome of receiving river water than to the resistomes of other habitats, and river water was the habitat most strongly impacted by the WWTPs effluent. The sulfonamide, beta-lactam, and aminoglycoside resistance genes were among the most abundant ARGs in the WWTP effluents, and especially aadA, sul1, and class A beta-lactamase genes showed significantly increased abundance in the river water of downstream compared to upstream locations (p < 0.05). However, this was not the case for the sediment, biofilm, and amphipod gut habitats. Accordingly, evidence for accumulation or enrichment of ARGs through the riverine food web was not identified. Our study suggests that monitoring riverine antimicrobial resistance determinants could be conducted using "co-occurrence" of aadA, sul1, and class A beta-lactamase genes as an indicator of wastewater-related pollution and should focus on the water as the most affected habitat.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".