Environmentally Relevant Antibiotic Concentrations Exert Stronger Selection Pressure on River Biofilm Resistomes than AMR-Reservoir Effluents
Bibliographic record
Abstract
Freshwater environments are primary receiving systems of wastewater and effluents, which carry low concentrations of antibiotics and antimicrobial-resistant (AMR) bacteria and genes. Aquatic microbial communities are thus exposed to environmentally relevant concentrations of antibiotics (ERCA) that presumably influence the acquisition and spread of environmental AMR. Here, we analyzed ERCA exposure with and without the additional presence of municipal wastewater treatment plant effluent (W) and swine manure run-off (M) on aquatic biofilm resistomes. Microscopic analyses revealed decreased taxonomic diversity and biofilm structural integrity, while metagenomic analysis revealed an increased abundance of resistance, virulence, and mobile element-related genes at the highest ERCA exposure levels, with less notable impacts observed when solely exposed to W or M effluents. Microbial function predictions indicated increased gene abundance associated with energy and cell membrane metabolism and heavy metal resistance under ERCA conditions. In silico predictions of increased resistance mechanisms did not correlate with observed phenotypic resistance patterns when whole communities were exposed to antimicrobial susceptibility testing. This reveals important insight into the complexity of whole-community coordination of physical and genetic responses to selective pressures. Lastly, the environmental AMR risk assessment of metagenomic data revealed a higher risk score for biofilms grown at sub-MIC antibiotic conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".