Effects of Alkylation on Potency of Benz[a]anthracene for AhR2 Transactivation in Nine Species of Freshwater Fish
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are naturally occurring or anthropogenic organic chemicals that can activate the aryl hydrocarbon receptor 2 (AhR2) and induce toxicity in fishes. Alkyl PAHs are more abundant than nonalkylated PAHs in certain environmental matrices and there is growing evidence that alkylation can increase potency, dependent on the position of alkylation. However, it is unknown if the effect of alkylation on potency is conserved across species. In addition, relatively little is known regarding the extent of interspecies variation in sensitivity to PAHs and alkyl PAHs. Therefore, objectives of the present study were to characterize potency of benz[a]anthracene (BAA) and three alkylated homologues representing different alkylation positions in nine phylogenetically diverse species of fish using a standardized in vitro AhR2 transactivation assay. BAA and each alkylated homologue activated the AhR2 in a concentration-dependent manner in each species. Position-dependent effects on potency were observed in every species, but these effects were not consistent across species. Interspecies variation in sensitivity to AhR2 activation by each PAH was observed and ranged by up to 561-fold. Alkylation both increased and decreased the range of interspecies variation and sensitivity, but the potency of each alkylated homologue relative to BAA ranged by less than an order of magnitude among species. These results represent an early step toward the consideration of alkylated homologues for more objective ecological risk assessments of PAHs to native fishes. Environ Toxicol Chem 2023;42:1575-1585. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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".