The PPARγ agonist rosiglitazone enhances rat brown adipose tissue lipogenesis from glucose without altering glucose uptake
Bibliographic record
Abstract
We investigated the mechanisms whereby peroxisome proliferator-activated receptor-gamma (PPARgamma) agonism affects glucose and lipid metabolism in brown adipose tissue (BAT) by studying the impact of PPARgamma activation on BAT glucose uptake and metabolism, lipogenesis, and mRNA levels plus activities of enzymes involved in triacylglycerol (TAG) synthesis. Interscapular BAT of rats treated or not with rosiglitazone (15 mg*kg(-1).day(-1), 7 days) was evaluated in vivo for glucose uptake and lipogenesis and in vitro for glucose metabolism, gene expression, and activities of glycerolphosphate acyltransferase (GPAT), phosphatidate phosphatase-1 (PAP or lipin-1), and diacylglycerol acyltransferase (DGAT). Rosiglitazone increased BAT mass without affecting whole tissue glucose uptake. BAT glycogen content (-80%), its synthesis from glucose (-50%), and mRNA levels of UDP-glucose pyrophosphorylase (-40%), which generates UDP-linked glucose for glycogen synthesis, were all reduced by rosiglitazone. In contrast, BAT TAG-glycerol synthesis in vivo and glucose incorporation into TAG-glycerol in vitro were stimulated by the agonist along with the activities and mRNA levels of glycerol 3-phosphate-generating phosphoenolpyruvate carboxykinase and glycerokinase. Furthermore, rosiglitazone markedly increased the activities of GPAT and DGAT but not those of lipin-1-mediated PAP-1, enzymes involved in the sequential acylation of glycerol 3-phosphate and TAG synthesis. Because an adequate supply of fatty acids is essential for BAT nonshivering thermogenesis, the enhanced ability of BAT to synthesize TAG under PPARgamma activation may constitute an important mechanism by which lipid substrates are stored in preparation for an eventual thermogenic activation.
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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) | 0.001 | 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".