Deletion of Myeloid GSK3α Attenuates Atherosclerosis and Promotes an M2 Macrophage Phenotype
Bibliographic record
Abstract
OBJECTIVE: Glycogen synthase kinase (GSK)-3α/β has been implicated in the pathogenesis of diabetes mellitus, cancer, Alzheimer, and atherosclerosis. The tissue- and homolog-specific functions of GSK3α and β in atherosclerosis are unknown. This study examines the effect of hepatocyte or myeloid cell deletion of GSK3α or GSK3β on atherosclerosis in low-density lipoprotein receptor (LDLR)(-/-) mice. APPROACH AND RESULTS: We ablated GSK3α or GSK3β expression in hepatic or myeloid cells of LDLR(-/-) mice, and mice were fed a high-fat diet for 10 weeks. GSK3α or GSK3β deficiency in hepatic or myeloid cells did not affect metabolic parameters, including plasma lipid levels. Hepatic deletion of GSK3α or GSK3β did not affect the development of atherosclerosis or hepatic lipid content. Myeloid deletion of GSK3α, but not of GSK3β, reduced atherosclerotic lesion volume and lesion complexity. Mice lacking GSK3α in myeloid cells had a less inflammatory and more anti-inflammatory plasma cytokine profile. Macrophages within atherosclerotic lesions of myeloid GSK3α-deficient mice, but not of GSK3β-deficient mice, displayed reduced expression of markers associated with M1 macrophage polarization and enhanced expression of the M2 markers. Finally, bone marrow-derived macrophages were isolated and differentiated into classical M1 macrophages or alternative M2 macrophages in vitro. GSK3α deletion, but not GSK3β deletion, attenuated the expression of genes associated with M1 polarization while promoting the expression of genes associated with M2 polarization by modulating STAT3 and STAT6 activation. CONCLUSIONS: Our findings suggest that deletion of myeloid GSK3α attenuates the progression of atherosclerosis by promoting an M2 macrophage phenotype.
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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) | 0.001 | 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".