Endoplasmic Reticulum Stress and Glycogen Synthase Kinase-3β Activation in Apolipoprotein E–Deficient Mouse Models of Accelerated Atherosclerosis
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to examine the role of endoplasmic reticulum (ER) stress signaling and the contribution of glycogen synthase kinase (GSK)-3β activation in hyperglycemic, hyperhomocysteinemic, and high-fat-fed apolipoprotein E-deficient (apoE(-/-)) mouse models of accelerated atherosclerosis. METHODS AND RESULTS: Female apoE(-/-) mice received multiple low-dose injections of streptozotocin (40 μg/kg) to induce hyperglycemia, methionine-supplemented drinking water (0.5% wt/vol) to induce hyperhomocysteinemia, or a high-fat (21% milk fat+0.2% cholesterol) diet to induce relative dyslipidemia. A subset of mice from each group was supplemented with sodium valproate (625 mg/kg), a compound with GSK3 inhibitory activity. At 15 and 24 weeks of age, markers of ER stress, lipid accumulation, GSK3β phosphorylation, and GSK3β activity were analyzed in liver and aorta. Atherosclerotic lesions were examined and quantified. Hyperglycemia, hyperhomocysteinemia, and high-fat diet significantly enhanced GSK3β activity and also increased hepatic steatosis and atherosclerotic lesion volume compared with controls. Valproate supplementation blocked GSK3β activation and attenuated the development of atherosclerosis and the accumulation of hepatic lipids in each of the models examined. The mechanism by which GSK3β activity is regulated in these models likely involves alterations in phosphorylation at serine 9 and tyrosine 216. CONCLUSIONS: These findings support the existence of a common mechanism of accelerated atherosclerosis involving ER stress signaling through activation of GSK3β. Furthermore, our results suggest that atherosclerosis can be attenuated by modulating GSK3β phosphorylation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".