RAGE impairs murine diabetic atherosclerosis regression and implicates IRF7 in macrophage inflammation and cholesterol metabolism
Bibliographic record
Abstract
Despite advances in lipid-lowering therapies, people with diabetes continue to experience more limited cardiovascular benefits. In diabetes, hyperglycemia sustains inflammation and preempts vascular repair. We tested the hypothesis that the receptor for advanced glycation end-products (RAGE) contributes to these maladaptive processes. We report that transplantation of aortic arches from diabetic, Western diet-fed Ldlr-/- mice into diabetic Ager-/- (Ager, the gene encoding RAGE) versus WT diabetic recipient mice accelerated regression of atherosclerosis. RNA-sequencing experiments traced RAGE-dependent mechanisms principally to the recipient macrophages and linked RAGE to interferon signaling. Specifically, deletion of Ager in the regressing diabetic plaques downregulated interferon regulatory factor 7 (Irf7) in macrophages. Immunohistochemistry studies colocalized IRF7 and macrophages in both murine and human atherosclerotic plaques. In bone marrow-derived macrophages (BMDMs), RAGE ligands upregulated expression of Irf7, and in BMDMs immersed in a cholesterol-rich environment, knockdown of Irf7 triggered a switch from pro- to antiinflammatory gene expression and regulated a host of genes linked to cholesterol efflux and homeostasis. Collectively, this work adds a new dimension to the immunometabolic sphere of perturbations that impair regression of established diabetic atherosclerosis and suggests that targeting RAGE and IRF7 may facilitate vascular repair in diabetes.
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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 | 0.001 | 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".