Transcriptional repression of TRH promoter function by T3: analysis by in vivo gene transfer
Bibliographic record
Abstract
We consider how an integrated in vivo model can be used to study the specific transcriptional effects of specific receptors in neuroendocrine systems. Our example is the role of thyroid receptor (TR) isoforms in mediating negative feedback effects of T3 on TRH (thyrotropin releasing hormone) expression. The in vivo transfection method employed polyethylenimine (PEI) to introduce genes directly into specifc regions of the brains of mice, rats, and Xenopus tadpoles. In the mouse model, the technique has served to examine TR effects on TRH transcription and on the pituitary-thyroid axis end point: thyroid hormone secretion. When a TRH-luciferase construct is introduced into the hypothalami of newborn mice TRH-luciferase transcription is regulated physiologically, being significantly increased in hypothyroidism and decreased in T3-treated animals. When various T3-binding forms of TRβ or TRα are expressed in the hypothalamus, all TRβ isoforms give T3-dependent regulation of TRH transcription, whereas TRα isoforms block T3-dependent transcription. Moreover, TR transcriptional effects are correlated with physiological consequences on circulating T4. Thus, somatic gene transfer shows TR subtypes to have distinct, physiologically relevant effects on TRH transcription. The approach is an appealing alternative to germinal transgenesis for studying specific neuroendocrine regulations at defined developmental stages in different species.Key words: thyroid hormone, TRH, mouse central nervous system, non viral gene transfer, polyethylenimine.
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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 | 0.001 |
| 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".