Families of Nuclear Receptors in Vertebrate Models: Characteristic and Comparative Toxicological Perspective
Bibliographic record
Abstract
Various synthetic chemicals are ligands for nuclear receptors (NRs) and can cause adverse effects in vertebrates mediated by NRs. While several model vertebrates, such as mouse, chicken, western clawed frog and zebrafish, are widely used in toxicity testing, few NRs have been well described for most of these classes. In this report, NRs in genomes of 12 vertebrates are characterized via bioinformatics approaches. Although numbers of NRs varied among species, with 40-42 genes in birds to 66-74 genes in teleost fishes, all NRs had clear homologs in human and could be categorized into seven subfamilies defined as NR0B-NR6A. Phylogenetic analysis revealed conservative evolutionary relationships for most NRs, which were consistent with traditional morphology-based systematics, except for some exceptions in Dolphin (Tursiops truncatus). Evolution of PXR and CAR exhibited unexpected multiple patterns and the existence of CAR possibly being traced back to ancient lobe-finned fishes and tetrapods (Sarcopterygii). Compared to the more conservative DBD of NRs, sequences of LBD were less conserved: Sequences of THRs, RARs and RXRs were ≥90% similar to those of the human, ERs, AR, GR, ERRs and PPARs were more variable with similarities of 60%-100% and PXR, CAR, DAX1 and SHP were least conserved among species.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".