Interplay between liganded and orphan nuclear receptors controls reproductive pathways
Bibliographic record
Abstract
Nuclear receptors are transcription factors that belong to an evolutionary ancient superfamily. These proteins, which are even present in primitive metazoans, are implicated in all levels of cell fate: proliferation, differentiation, and apoptosis. Some of these nuclear receptors behave as ligand-inducible transcription factors, as they have acquired during evolution the ability to bind ligands. This is the case for some proteins that recognize small hydrophobic signaling molecules, and particularly the estrogen receptor (ER or NR3A1), which regulates the target gene's transcription rate under estrogen binding. It is now known that the ER alone regulates the transcription of many genes, such as those implicated in reproductive functions. However, this ER-mediated signaling pathway could be modulated by other transcription factors. Our work has established that two other orphan nuclear receptors (SF-1 or NR5A1 and the COUP-TFs, NR2F1 and NR2F2) can enhance two ER-regulated genes implicated in salmonid reproductive functions: the ER gene itself, and the sGTHIIβ gene. Moreover, some xenoestrogens could disturb these regulations. Therefore, our data contribute to the concept that interplay between nuclear receptors is an important event for the transcriptional regulation of genes controlling cellular functions.Key words: reproduction, estrogen receptor, SF-1, COUP-TFI, gene transcription, xenobiotics.
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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".