Differential Regulation of Macrophage Peroxisome Proliferator–Activated Receptor Expression by Glucose
Bibliographic record
Abstract
Peroxisome proliferator-activated receptors (PPARs) are implicated in several metabolic disorders with altered glucose and lipid metabolism, including atherosclerosis and diabetes. In the present study, we evaluated the in vitro and ex vivo effects of high glucose concentrations on macrophage PPAR mRNA expression. Exposition of monocyte-derived macrophages isolated from healthy donors to a high glucose environment led to an increase in PPARalpha and PPARbeta mRNA expression. In contrast, this treatment significantly decreased human macrophage PPARgamma mRNA expression. Overexpression of PPARalpha and PPARbeta mRNA and inhibition of PPARgamma mRNA expression were also observed in monocyte-derived macrophages isolated from patients with type 2 diabetes. Because high glucose and PPARalpha agonists increase lipoprotein lipase (LPL) gene expression, the role of PPARalpha in the glucose-mediated upregulation of macrophage LPL gene expression was next evaluated. Incubation of murine J774 macrophages with high glucose concentrations increased the expression of PPARalpha at the mRNA and protein levels and enhanced nuclear protein binding to the peroxisome proliferator responsive element of the LPL promoter. Incubation of nuclear extracts in the presence of anti-PPARalpha and anti-PPARbeta antibodies decreased glucose-stimulated nuclear protein binding to the peroxisome proliferator responsive element. These results demonstrate that glucose is an important regulator of macrophage PPAR expression and suggest a role of PPARalpha and PPARbeta in the upregulation of macrophage LPL by glucose. Dysregulation of macrophage PPAR expression in type 2 diabetes may contribute, by altering arterial lipid metabolism and inflammatory response, to the accelerated atherosclerosis associated with diabetes.
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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 | 0.001 | 0.000 |
| 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 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".