Lipolytic Products Activate Peroxisome Proliferator-activated Receptor (PPAR) α and δ in Brown Adipocytes to Match Fatty Acid Oxidation with Supply
Bibliographic record
Abstract
β-Adrenergic receptors (β-ARs) promote brown adipose tissue (BAT) thermogenesis by mobilizing fatty acids and inducing the expression of oxidative genes. β-AR activation increases the expression of oxidative genes by elevating cAMP, but whether lipolytic products can modulate gene expression is not known. This study examined the role that adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL) plays in the induction of gene expression. Activation of brown adipocytes by β-AR agonism or 8-bromo-cyclic AMP increased the expression of PGC1α, PDK4, PPARα, uncoupling protein 1 (UCP1), and neuron-derived orphan receptor-1 (NOR-1), and concurrent inhibition of HSL reduced the induction of PGC1α, PDK4, PPARα, and UCP1 but not NOR-1. Similar results were observed in the BAT of mice following pharmacological or genetic inhibition of HSL and in brown adipocytes with stable knockdown of ATGL. Conversely, treatments that increase endogenous fatty acids elevated the expression of oxidative genes. Pharmacological antagonism and siRNA knockdown indicate that PPARα and PPARδ modulate the induction of oxidative genes by β-AR agonism. Using a live cell fluorescent reporter assay of PPAR activation, we demonstrated that ligands for PPARα and -δ, but not PPARγ, were rapidly generated at the lipid droplet surface and could transcriptionally activate PPARα and -δ. Knockdown of ATGL reduced cAMP-mediated induction of genes involved in fatty acid oxidation and oxidative phosphorylation. Consequently, ATGL knockdown reduced maximal oxidation of fatty acids, but not pyruvate, in response to cAMP stimulation. Overall, the results indicate that lipolytic products can activate PPARα and PPARδ in brown adipocytes, thereby expanding the oxidative capacity to match enhanced fatty acid supply.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".