Variation in the transcriptome response and detoxification gene diversity drives pesticide tolerance in fishes
Bibliographic record
Abstract
Abstract Pesticides are critical for invasive species management, but often have negative effects on non-target native biota. Tolerance to pesticides should have an evolutionary basis, but this is poorly understood. Invasive sea lamprey ( Petromyzon marinus ) populations in North America have been controlled with a pesticide lethal to them at lower concentrations than native fishes. We addressed how interspecific variation in gene expression and detoxification gene diversity confer differential pesticide sensitivity in two fish species. We exposed sea lamprey and bluegill ( Lepomis macrochirus ), a tolerant native species, to TFM, a pesticide commonly used in sea lamprey control, and used whole-transcriptome sequencing of gill and liver to characterize the cellular response. Comparatively, bluegill exhibited a larger number of detoxification genes expressed and a larger number of responsive transcripts overall, which likely contributes to greater tolerance to TFM. Understanding the genetic and physiological basis for pesticide tolerance is crucial for managing invasive species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".