Macrophage Sortilin Promotes LDL Uptake, Foam Cell Formation, and Atherosclerosis
Bibliographic record
Abstract
RATIONALE: Noncoding gene variants at the SORT1 locus are strongly associated with low-density lipoprotein cholesterol (LDL-C) levels, as well as with coronary artery disease. SORT1 encodes a protein called sortilin, and hepatic sortilin modulates LDL metabolism by targeting apolipoprotein B-containing lipoproteins to the lysosome. Sortilin is also expressed in macrophages, but its role in macrophage uptake of LDL and in atherosclerosis independent of plasma LDL-C levels is unknown. OBJECTIVE: To determine the effect of macrophage sortilin expression on LDL uptake, foam cell formation, and atherosclerosis. METHODS AND RESULTS: We crossed Sort1(-/-) mice onto a humanized Apobec1(-/-); hAPOB transgenic background and determined that Sort1 deficiency on this background had no effect on plasma LDL-C levels but dramatically reduced atherosclerosis in the aorta and aortic root. To test whether this effect was a result of macrophage sortilin deficiency, we transplanted Sort1(-/-);LDLR(-/-) or Sort1(+/+);LDLR(-/-) bone marrow into Ldlr(-/-) mice and observed a similar reduction in atherosclerosis in mice lacking hematopoetic sortilin without an effect on plasma LDL-C levels. In an effort to determine the mechanism by which hematopoetic sortilin deficiency reduced atherosclerosis, we found no effect of sortilin deficiency on macrophage recruitment or lipopolysaccharide-induced cytokine release in vivo. In contrast, sortilin-deficient macrophages had significantly reduced uptake of native LDL ex vivo and reduced foam cell formation in vivo, whereas sortilin overexpression in macrophages resulted in increased LDL uptake and foam cell formation. CONCLUSIONS: Macrophage sortilin deficiency protects against atherosclerosis by reducing macrophage uptake of LDL. Sortilin-mediated uptake of native LDL into macrophages may be an important mechanism of foam cell formation and contributor to atherosclerosis development.
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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) | 0.002 | 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".