Fate of Tetracycline Resistance Genes in Aquatic Systems: Migration from the Water Column to Peripheral Biofilms
Bibliographic record
Abstract
Antibiotic resistance genes (ARGs) are emerging contaminants that are being found at elevated levels in sediments and other aquatic compartments in areas of intensive agricultural and urban activity. However, little quantitative data exist on the migration and attenuation of ARGs in natural ecosystems, which is central to predicting their fate after release into receiving waters. Here we examined the fate of tetracycline-resistance genes in bacterial hosts released in cattle feedlot wastewater using field-scale mesocosms to quantify ARG attenuation rate in the water column and also the migration of ARGs into peripheral biofilms. Feedlot wastewater was added to fifteen cylindrical 11.3-m3 mesocosms (some of which had artificial substrates) simulating five different receiving water conditions (in triplicate), and the abundance of six resistance genes (tet(O), tet(W), tet(M), tet(Q), tet(B), and tet(L)) and 16S-rRNA genes was monitored for 14 days. Mesocosm treatments were varied according to light supply, microbial supplements (via river water additions), and oxytetracycline (OTC) level. First-order water column disappearance coefficients (kd) for the sum of the six genes (tetR) were always higher in sunlight than in the dark (-0.72 d(-1) and -0.51 d(-1), respectively). However, water column kd varied among genes (tet(O) < tet(W) < tet(M) < tet(Q); tet(B) and tet(L) were below detection) and some genes, particularly tet(W), readily migrated into biofilms, suggesting that different genes be considered separately and peripheral compartments be included in future fate models. This work provides the first quantitative field data for modeling ARG fate in aquatic systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".