Indirect Evidence of Transposon-Mediated Selection of Antibiotic Resistance Genes in Aquatic Systems at Low-Level Oxytetracycline Exposures
Bibliographic record
Abstract
Subinhibitory levels of antibiotics can promote the development of antibiotic resistance in bacteria. However, it is unclear whether antibiotic concentrations released into aquatic systems exert adequate pressure to select populations with resistance traits. To examine this issue, 15 mesocosms containing pristine surface water were treated with oxytetracycline (OTC) for 56 days at five levels (0, 5, 20, 50, and 250 microg L(-1)), and six tetracycline-resistance genes (tet(B), tet(L), tet(M), ted(O), tet(Q), and tet(W)), the sum of those genes (tet(R)), "total" 16S-rRNA genes, and transposons (Tn916 and Tn 1545) were monitored using real-time PCR. Absolute water-column resistance-gene abundances did not change at any OTC exposure. However, an increase was observed in the ratio of tet(R) to 16S-rRNA genes in the 250 microg L(-1) OTC units, and an increase in the selection rate of Tc(r) genes (relative to 16S-rRNA genes) was seen when OTC levels were at 20 microg L(-1). Furthermore, tet(M) and Tn916/1545 gene abundances correlated among all treatments (r2 = 0.701, p = 0.05), and there were similar selection patterns of tetR and Tn916/1545 genes relative to the OTC level, suggesting a possible mechanism for retention of specific resistance genes within the systems.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".