Inhibition of malonyl-CoA decarboxylase reduces the inflammatory response associated with insulin resistance
Bibliographic record
Abstract
We previously showed that genetic inactivation of malonyl-CoA decarboxylase (MCD), which regulates fatty acid oxidation, protects mice against high-fat diet-induced insulin resistance. Development of insulin resistance has been associated with activation of the inflammatory response. Therefore, we hypothesized that the protective effect of MCD inhibition might be caused by a favorable effect on the inflammatory response. We examined if pharmacological inhibition of MCD protects neonatal cardiomyocytes and peritoneal macrophages against inflammatory-induced metabolic perturbations. Cardiomyocytes and macrophages were treated with LPS to induce an inflammatory response, in the presence or absence of an MCD inhibitor (CBM-301106, 10 μM). Inhibition of MCD attenuated the LPS-induced inflammatory response in cardiomyocytes and macrophages. MCD inhibition also prevented LPS impairment of insulin-stimulated glucose uptake in cardiomyocytes and increased phosphorylation of Akt. Additionally, inhibition of MCD strongly diminished LPS-induced activation of palmitate oxidation. We also found that treatment with an MCD inhibitor prevented LPS-induced collapse of total cellular antioxidant capacity. Interestingly, treatment with LPS or an MCD inhibitor did not alter intracellular triacylglycerol content. Furthermore, inhibition of MCD prevented LPS-induced increases in the level of ceramide in cardiomyocytes and macrophages while also ameliorating LPS-initiated decreases in PPAR binding. This suggests that the anti-inflammatory effect of MCD inhibition is mediated via accumulation of long-chain acyl-CoA, which in turn stimulates PPAR binding. Our results also demonstrate that pharmacological inhibition of MCD is a novel and promising approach to treat insulin resistance and its associated metabolic complications.
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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) | 0.001 | 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) | 0.001 | 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".