Pkr‐like endoplasmic reticulum kinase (PERK) and glycogen synthase kinase‐3α/β signaling regulate atherosclerosis development (278.5)
Bibliographic record
Abstract
Recently, a role for endoplasmic reticulum (ER) stress signaling through glycogen synthase kinase (GSK)‐3α/β has been proposed in atherogenesis; this study sought to delineate the underlying molecular mechanisms. Cultured THP1‐derived macrophages were treated with the ER stress‐inducing agents, glucosamine, thapsigargin or palmitate in the presence or absence of inhibitors of the unfolded protein response (UPR) pathways (PERK, IRE, ATF6) or GSK3α/β (CT99021). ER stress enhanced GSK3α/β activity while inhibition of the PERK pathway, but not the IRE or ATF6 pathways, attenuated GSK3α/β activity. GSK3α/β inhibition did not affect adaptive components of the UPR but did repress the expression of factors downstream of PERK (ATF4 and CHOP). ER stress enhanced the mRNA expression of genes controlling lipid biosynthesis and uptake; while GSK3α/β inhibition attenuated this effect. GSK3α/β inhibition blocked ER stress‐induced cholesterol accumulation in macrophages. To investigate the roles of macrophage GSK3α and GSK3β in a mouse model of atherosclerosis, LDLR‐/‐ myeloid cell GSK3α knockout (LDLR‐/‐GSK3αflox/floxLyzMCre+/‐) and GSK3β knockout (LDLR‐/‐GSK3βflox/floxLyzMCre+/‐) mice were placed on a high fat diet. Myeloid cell GSK3α deficiency, but not GSK3β deficiency, attenuated atherogenesis. Our findings support a role for macrophage PERK ‐ GSK3α signaling in atherosclerosis development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".