Transcriptomic impacts and potential routes of detoxification in a lampricide-tolerant teleost exposed to TFM and niclosamide
Bibliographic record
Abstract
Abstract Sea lamprey ( Petromyzon marinus ) control in the Laurentian Great Lakes of North America often relies on the application of 3-trifluoromethyl-4-nitrophenol (TFM) and niclosamide mixtures to kill larval sea lamprey. Selectivity of TFM against lampreys appears to be due to differential detoxification ability in these jawless fishes compared to bony fishes, particularly teleosts. However, the proximate mechanisms of tolerance to the TFM and niclosamide mixture and the mechanisms of niclosamide toxicity on its own are poorly understood, especially among non-target fishes. Here, we used RNA sequencing to identify specific mRNA transcripts and functional processes that responded to niclosamide or a TFM:niclosamide mixture in bluegill ( Lepomis macrochirus ). Bluegill were exposed to niclosamide or TFM:niclosamide mixture, along with a time-matched control group, and gill and liver tissues were sampled at 6, 12, and 24 h. We summarized the whole-transcriptome patterns through gene ontology (GO) term enrichment and through differential expression of detoxification genes. The niclosamide treatment resulted in an upregulation of several transcripts associated with detoxification ( cyp, ugt, sult, gst ), which may help explain the relatively high detoxification capacity in bluegill. Conversely, the TFM:niclosamide mixture resulted in an enrichment of processes related to arrested cell cycle and growth, and cell death alongside a diverse detoxification gene response. Detoxification of both lampricides likely involves the use of phase I and II biotransformation genes. Our findings strongly suggest that the unusually high tolerance of bluegill to lampricides is due to these animals having an inherently high capacity and flexible detoxification response to such compounds.
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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 | 0.000 |
| 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".