Triclosan is the Predominant Antibacterial Compound in Ontario Sewage Sludge
Bibliographic record
Abstract
Sewage treatment plants (STPs) accumulate both antibiotic and nonantibiotic antimicrobial compounds that can select for antibiotic resistant bacteria. Herein, we aimed to identify the predominant antibacterial compounds impacting E. coli from Ontario sewage sludge consisting of thousands of unknown compounds. Among the 10 extracted sludge samples, 6 extracts exerted significant growth inhibition effects in E. coli . A total of 103 compounds were tentatively detected across the 10 sludge samples by suspect screening, among which the bacterial enoyl-ACP reductase (FabI) inhibitor triclocarban was detected at the highest abundance. A hypomorphic FabI knockdown E. coli strain was highly susceptible to the sludge extracts, confirming FabI inhibitors as the primary antibacterial compounds in the sludge. Protein affinity pulldown identified triclosan as the major ligand binding to a His-tagged FabI protein from the sludge, despite the higher abundance of triclocarban in the same samples. Effect-directed analysis was used to determine the contributions of triclosan to the observed antibacterial potencies. Antibacterial effects were only detected in F 17 and F 18 across 20 fractions, which was consistent with the elution of triclosan and triclocarban in the same two fractions. Further, potency mass balance analysis confirmed that triclosan explained the majority (58–113%) of inhibition effects from sludge extracts. This study highlighted triclosan as the predominant antibacterial compound in sewage sludge impacting E. coli despite the co-occurrence of numerous other antibiotics and nonantibiotics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".