Activation of nucleotide oligomerization domain containing protein 1 induces lipolysis through NF-κB and the lipolytic PKA activation in 3T3-L1 adipocytes
Bibliographic record
Abstract
Obesity is associated with chronic inflammation. Toll-like receptors (TLR) and NOD-like receptors (NLR) are two families of pattern recognition receptors that play important roles in the immune response and inflammation in adipocytes. Activation of TLR4 has been shown to stimulate lipolysis from adipose tissue or adipocytes. However, effects of activation of nucleotide-oligomerization domain containing protein 1 (NOD1), one of the prominent members of NLRs, on adipocyte lipolysis have not been studied. Here we report that NOD1 activation by the synthetic ligands (Tri-DAP and C12-iEDAP) stimulated lipolysis in 3T3-L1 adipocytes in a time- and dose-dependent manner. C12-iEDAP-induced lipolysis was attenuated with NOD1 siRNA knockdown, demonstrating the specificity of the effects. Moreover, inhibition of the protein kinase A (PKA)/hormone sensitive lipase (HSL) and NF-κB pathways by the pharmacological inhibitors attenuated the lipolytic effects of C12-iEDAP. Furthermore, we show NOD1 activation induced PKA activation independent of cAMP production and inhibition of NF-κB pathways attenuated phosphorylation of selected PKA lipolytic targets (phosphorylation of Perilipin Ser 517 and HSL Ser 563). Taken together, our results demonstrate a novel role of NOD1 activation, via NF-κB/PKA lipolytic activation, in inducing lipolysis in adipocytes and suggest that NOD1 activation may contribute to dyslipidemia in obesity.
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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) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".