Intracellular fatty acids suppress β-adrenergic induction of PKA-targeted gene expression in white adipocytes
Bibliographic record
Abstract
β-Adrenergic receptor (β-AR) activation elevates cAMP levels in fat cells and triggers both metabolic and transcriptional responses; however, the potential interactions between these pathways are poorly understood. This study investigated whether lipolysis affects β-AR-mediated gene expression in adipocytes. Acute β(3)-adrenergic receptor (β(3)-AR) stimulation with CL 316,243 (CL) increased expression of PKA-targeted genes PCG-1α, UCP1, and NOR-1 in mouse white fat. Limiting lipolysis via inhibition of hormone-sensitive lipase (HSL), a direct target of PKA, sharply potentiated CL induction of PCG-1α, UCP1, and NOR-1. CL also induced greater expression of PKA-targeted genes in white fat of HSL-null mice compared with wild-type littermates, further indicating that HSL activity limits PKA-mediated gene expression. Inhibiting HSL in 3T3-L1 adipocytes also potentiated the induction of PGC-1α, UCP1, and NOR-1 by β-AR activation, as did siRNA knockdown of adipose triglyceride lipase, the rate-limiting enzyme for lipolysis. Conversely, treatments that promote intracellular fatty acid accumulation suppressed induction of PGC-1α and UCP1 through β-AR stimulation. Analysis of β-adrenergic signaling indicated that excessive intracellular fatty acid production inhibits adenylyl cyclase activity and thereby reduces PKA signaling to the nucleus. Lastly, partially limiting lipolysis by inhibition of HSL increased the induction of oxidative gene expression and mitochondrial electron transport chain activity in white adipose tissue and facilitated fat loss in mice treated for 5 days with CL. Overall, our results demonstrate that fatty acids limit the upregulation of β-AR-responsive genes in white adipocytes and suggest that limiting lipolysis may be a novel means of enhancing β-AR signaling.
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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".