Differences in Activation of Aryl Hydrocarbon Receptors of White Sturgeon Relative to Lake Sturgeon Are Predicted by Identities of Key Amino Acids in the Ligand Binding Domain
Bibliographic record
Abstract
Dioxin-like compounds (DLCs) are pollutants of global environmental concern. DLCs elicit their adverse outcomes through activation of the aryl hydrocarbon receptor (AhR). However, there is limited understanding of the mechanisms that result in differences in sensitivity to DLCs among different species of fishes. Understanding these mechanisms is critical for protection of the diversity of fishes exposed to DLCs, including endangered species. This study investigated specific mechanisms that drive responses of two endangered fishes, white sturgeon (Acipenser transmontanus) and lake sturgeon (Acipenser fulvescens) to DLCs. It determined whether differences in sensitivity to activation of AhRs (AhR1 and AhR2) can be predicted based on identities of key amino acids in the ligand binding domain (LBD). White sturgeon were 3- to 30-fold more sensitive than lake sturgeon to exposure to 5 different DLCs based on activation of AhR2. There were no differences in sensitivity between white sturgeon and lake sturgeon based on activation of AhR1. Adverse outcomes as a result of exposure to DLCs have been shown to be mediated through activation of AhR2, but not AhR1, in all fishes studied to date. This indicates that white sturgeon are likely to have greater sensitivity in vivo relative to lake sturgeon. Homology modeling and in silico mutagenesis suggests that differences in sensitivity to activation of AhR2 result from differences in key amino acids at position 388 in the LBD of AhR2 of white sturgeon (Ala-388) and lake sturgeon (Thr-388). This indicates that identities of key amino acids in the LBD of AhR2 could be predictive of both in vitro activation by DLCs and in vivo sensitivity to DLCs in these, and potentially other, fishes.
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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".