Metagenomic assessment of full-scale wastewater treatment plants identifies sentinel antibiotic resistance gene families for monitoring reclaimed wastewater and treated sludge.
Bibliographic record
Abstract
The new European (EU) regulation on water reuse explicitly incorporates antimicrobial resistance (AMR) into routine monitoring and risk management, creating an urgent need to define target antibiotic resistance genes (ARGs) for reclaimed irrigation water and agricultural sludge. However, existing global data largely focus on secondary effluents, providing little actionable evidence for reuse-oriented systems. Here, we present the first integrated framework combining targeted antibiotic residue analysis with shotgun metagenomics of the resistome, mobilome, and microbiome across full-scale reuse-oriented wastewater treatment plants (WWTPs) in Southern Europe to identify sentinel antibiotic resistance families for monitoring. Reclaimed effluents reflected low AMR exposure levels (<0.5 ARGs/cell), but mobile genetic element (MGE) levels comparable to secondary effluents (1-2 MGEs/cell). Effluent communities differed by WWTP configuration: membrane bioreactor combined with ultrafiltration favored nutrient-removal/oxidative-stress taxa and reduced transferable MGEs, whereas plants relying on physical separation (sand filtration or reverse osmosis) retained fecal-associated taxa and MGEs. Specific clinically relevant ARGs persisted after treatments, including aadA and aph(3'')-Ibs (resistance to aminoglycosides), ermB and mphA (resistance to macrolides), and blaOXA-129 (resistance to beta-lactams), which we identify as sentinel markers for monitoring reclaimed water and sludge. We advance a generalizable two-step framework, metagenomic discovery to identify sentinel markers, followed by targeted assays for streamlined surveillance, that provides the first operational blueprint for integrating AMR into water reuse management under the EU regulation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".