Reduced ATGL-mediated lipolysis attenuates β-adrenergic-induced AMPK signaling, but not the induction of PKA-targeted genes, in adipocytes and adipose tissue
Bibliographic record
Abstract
5'-AMP-activated protein kinase (AMPK) is activated as a consequence of lipolysis and has been shown to play a role in regulation of adipose tissue mitochondrial content. Conversely, the inhibition of lipolysis has been reported to potentiate the induction of protein kinase A (PKA)-targeted genes involved in the regulation of oxidative metabolism. The purpose of the current study was to address these apparent discrepancies and to more fully examine the relationship between lipolysis, AMPK, and the β-adrenergic-mediated regulation of gene expression. In 3T3-L1 adipocytes, the adipose tissue triglyceride lipase (ATGL) inhibitor ATGListatin attenuated the Thr(172) phosphorylation of AMPK by a β3-adrenergic agonist (CL 316,243) independent of changes in PKA signaling. Similarly, CL 316,243-induced increases in the Thr(172) phosphorylation of AMPK were reduced in adipose tissue from whole body ATGL-deficient mice. Despite reductions in the activation of AMPK, the induction of PKA-targeted genes was intact or, in some cases, increased. Similarly, markers of mitochondrial content and respiration were increased in adipose tissue from ATGL knockout mice independent of changes in the Thr(172) phosphorylation of AMPK. Taken together, our data provide evidence that AMPK is not required for the regulation of adipose tissue oxidative capacity in conditions of reduced fatty acid release.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".