PPARγ activation attenuates cold-induced upregulation of thyroid status and brown adipose tissue PGC-1α and D2
Bibliographic record
Abstract
Here, we investigated whether pharmacological PPARγ activation modulates key early events in brown adipose tissue (BAT) recruitment induced by acute cold exposure with the aim of unraveling the interrelationships between sympathetic and PPARγ signaling. Sprague-Dawley rats treated or not with the PPARγ ligand rosiglitazone (15 mg·kg(-1)·day(-1), 7 days) were kept at 23°C or exposed to cold (5°C) for 24 h and evaluated for BAT gene expression, sympathetic activity, thyroid status, and adrenergic signaling. Rosiglitazone did not affect the reduction in body weight gain and the increase in feed efficiency, Vo(2), and BAT sympathetic activity induced by 24-h cold exposure. Rosiglitazone strongly attenuated the increase in serum total and free T4 and T3 levels and BAT iodothyronine deiodinase type 2 (D2) and PGC-1α mRNA levels and potentiated the reduction in BAT thyroid hormone receptor (THR) β mRNA levels induced by cold. Administration of T3 to rosiglitazone-treated rats exacerbated the cold-induced increase in energy expenditure but did not restore a proper activation of D2 and PGC-1α, nor further increased uncoupling protein 1 expression. Regarding adrenergic signaling, rosiglitazone did not affect the changes in BAT cAMP content and PKA activity induced by cold. Rosiglitazone alone or in combination with cold increased CREB binding to DNA, but it markedly reduced the expression of one of its major coactivators, CREB binding protein. In conclusion, pharmacological PPARγ activation impairs short-term cold elicitation of BAT adrenergic and thyroid signaling, which may result in abnormal tissue recruitment and thermogenic activity.
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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 | 0.000 |
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".