Pesticides Inhibit Retinoic Acid Catabolism in PLHC-1 and ZFL Fish Hepatic Cell Lines
Bibliographic record
Abstract
The population of yellow perch (Perca flavescens) in lake Saint-Pierre (QC, Canada) has been dramatically declining since 1995 without any sign of recovery. Previous studies have shown disrupted retinoid (vitamin A) metabolic pathways in these fish, possibly due to the influence of pesticides. Our study aimed to evaluate the impact of some herbicides and neonicotinoids on retinoic acid catabolism in the fish hepatic cell lines PLHC-1 and ZFL. We hypothesized that pesticides accelerate the catabolism of retinoic acid through oxidative stress that exacerbates the oxidation of retinoic acid. Results obtained with talarozole, a specific CYP26A1 inhibitor, and ketoconazole, a generalist inhibitor of cytochrome-P450 enzymes, revealed that CYP26A1 is mainly responsible for retinoic acid catabolism in ZFL but not PLHC-1 cells. The impacts of pesticides on retinoic acid catabolism were evaluated by incubating the cells with all-trans-retinoic acid and two herbicides, atrazine and glyphosate, or three neonicotinoids, clothianidin, imidacloprid, and thiamethoxam. Intracellular thiols and lipid peroxidation were measured following pesticide exposure. The possible causal relation between oxidative stress and the perturbation of retinoic acid catabolism was investigated using the antioxidant N-acetylcysteine. The data revealed that pesticides inhibit retinoic acid catabolism, with the involvement of oxidative stress in the case of atrazine, imidacloprid, and thiamethoxam but not with clothianidin and glyphosate. Pesticides also affected the isomerization of all-trans-retinoic acid over time, leading to an increased proportion of active isomers. These results hint at a possible perturbation of retinoic acid catabolism in fish living in pesticide-contaminated waters, as suggested by several in vivo studies. Such a disruption of retinoid metabolism is worrying, given the numerous physiological pathways driven by retinoids.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".