Coupling of lipolysis and de novo lipogenesis in brown, beige, and white adipose tissues during chronic β3-adrenergic receptor activation
Bibliographic record
Abstract
Chronic activation of β3-adrenergic receptors (β3-ARs) expands the catabolic activity of both brown and white adipose tissue by engaging uncoupling protein 1 (UCP1)-dependent and UCP1-independent processes. The present work examined de novo lipogenesis (DNL) and TG/glycerol dynamics in classic brown, subcutaneous "beige," and classic white adipose tissues during sustained β3-AR activation by CL 316,243 (CL) and also addressed the contribution of TG hydrolysis to these dynamics. CL treatment for 7 days dramatically increased DNL and TG turnover similarly in all adipose depots, despite great differences in UCP1 abundance. Increased lipid turnover was accompanied by the simultaneous upregulation of genes involved in FAS, glycerol metabolism, and FA oxidation. Inducible, adipocyte-specific deletion of adipose TG lipase (ATGL), the rate-limiting enzyme for lipolysis, demonstrates that TG hydrolysis is required for CL-induced increases in DNL, TG turnover, and mitochondrial electron transport in all depots. Interestingly, the effect of ATGL deletion on induction of specific genes involved in FA oxidation and synthesis varied among fat depots. Overall, these studies indicate that FAS and FA oxidation are tightly coupled in adipose tissues during chronic adrenergic activation, and this effect critically depends on the activity of adipocyte ATGL.
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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".