Fortilin deficiency induces anti-atherosclerotic phenotypes in macrophages and protects hypercholesterolemic mice against atherosclerosis
Bibliographic record
Abstract
In the atherosclerotic intima, macrophages (MΦ) perpetuate chronic inflammation and cholesterol accumulation. Fortilin, a 172-amino-acid multifunctional protein, is abundant in the atherosclerotic intima and promotes atherogenesis, but its mechanism has remained unclear. Herein, we report that fortilin in MФ (fortilinMΦ) facilitates atherosclerosis by (a) enhancing MΦ survival, proliferation, and lipid uptake, leading to the accumulation of lipid-laden MФ in the intima and (b) inhibiting both the reverse transdifferentiation of MΦ into vascular smooth muscle cells (VSMCs) and the differentiation of mesenchymal stem cells (MSCs) into VSMCs. Mice lacking fortilinMΦ under genetically induced hypercholesterolemia (fortilinKO-MΦ-HC) exhibit drastically less atherosclerosis in their aortae compared to wild-type (fortilinWT-MΦ-HC) controls. Imaging mass cytometry reveals that the intima of fortilinKO-MΦ-HC mice contains fewer MФ but more VSMCs than that of fortilinWT-MФ-HC mice. Cell-based assays reveal that fortilin deficiency in MΦ augments low-density lipoprotein (LDL)-induced apoptosis, suppresses proliferation and foam cell formation, and boosts TGF-β1 production. Fortilin-deficient THP1 MΦ transdifferentiate into VSMCs, and their conditioned medium causes MSCs to differentiate toward VSMCs in a TGF-β1-dependent fashion. Together, these findings suggest that fortilinMΦ plays a complex facilitative role in atherogenesis and represents a viable molecular target for the treatment of atherosclerosis. Fortilin promotes atherogenesis by enhancing macrophage survival, proliferation, and lipid uptake while suppressing their transdifferentiation into vascular smooth muscle cells, leading to increased foam cell accumulation and reduced plaque stability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".