Protein kinase R-like endoplasmic reticulum kinase and glycogen synthase kinase-3α/β regulate foam cell formation
Bibliographic record
Abstract
Evidence suggests a causative role for endoplasmic reticulum (ER) stress in the development of atherosclerosis. This study investigated the potential role of glycogen synthase kinase (GSK)-3α/β in proatherogenic ER stress signaling. Thp1-derived macrophages were treated with the ER stress-inducing agents, glucosamine, thapsigargin, or palmitate. Using small-molecule inhibitors of specific unfolded protein response (UPR) signaling pathways, we found that protein kinase R-like ER kinase (PERK), but not inositol requiring enzyme 1 or activating transcription factor 6, is required for the activation of GSK3α/β by ER stress. GSK3α/β inhibition or siRNA-directed knockdown attenuated ER stress-induced expression of distal components of the PERK pathway. Macrophage foam cells within atherosclerotic plaques and isolated macrophages from ApoE(-/-) mice fed a diet supplemented with the GSK3α/β inhibitor valproate had reduced levels of C/EBP homologous protein (CHOP). GSK3α/β inhibition blocked ER stress-induced lipid accumulation and the upregulation of genes associated with lipid metabolism. In primary mouse macrophages, PERK inhibition blocked ER stress-induced lipid accumulation, whereas constitutively active S9A-GSK3β promoted foam cell formation and CHOP expression, even in cells treated with a PERK inhibitor. These findings suggest that ER stress-PERK-GSK3α/β signaling promotes proatherogenic macrophage lipid accumulation.
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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) | 0.000 | 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) | 0.001 | 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".