CXCL12 promotes atherosclerosis by downregulating ABCA1 expression via the CXCR4/GSK3β/β-cateninT120/TCF21 pathway
Bibliographic record
Abstract
CXC chemokine ligand 12 (CXCL12) is a member of the CXC chemokine family and mainly acts on cell chemotaxis. CXCL12 also elicits a proatherogenic role, but the molecular mechanisms have not been fully defined yet. We aimed to reveal if and how CXCL12 promoted atherosclerosis via regulating lipid metabolism. In vitro, our data showed that CXCL12 could reduce ABCA1 expression, and it mediated cholesterol efflux from THP-1-derived macrophages to apoA-I. Data from the luciferase reporter gene and chromatin immunoprecipitation assays revealed that transcription factor 21 (TCF21) stimulated the transcription of ABCA1 via binding to its promoter region, which was repressed by CXCL12. We found that CXCL12 increased the levels of phosphorylated glycogen synthase kinase 3β (GSK3β) and the phosphorylation of β-catenin at the Thr120 position. Inactivation of GSK3β or β-catenin increased the expression of TCF21 and ABCA1. Further, knockdown or inhibition of CXC chemokine receptor 4 (CXCR4) blocked the effects of CXCL12 on TCF21 and ABCA1 expression and the phosphorylation of GSK3β and β-catenin. In vivo, the overexpression of CXCL12 in Apoe −/− mice via lentivirus enlarged the atherosclerotic lesion area and increased macrophage infiltration in atherosclerotic plaques. We further found that the overexpression of CXCL12 reduced the efficiency of reverse cholesterol transport and plasma HDL-C levels, decreased ABCA1 expression in the aorta and mouse peritoneal macrophages (MPMs), and suppressed cholesterol efflux from MPMs to apoA-I in Apoe −/− mice. Collectively, these findings suggest that CXCL12 interacts with CXCR4 and then activates the GSK-3β/β-catenin T120 /TCF21 signaling pathway to inhibit ABCA1-dependent cholesterol efflux from macrophages and aggravate atherosclerosis. Targeting CXCL12 may be a novel and promising strategy for the prevention and treatment of atherosclerotic cardiovascular diseases.
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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 | 0.001 |
| 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".