Adventitial recruitment of Lyve-1− macrophages drives aortic aneurysm in an angiotensin-2-based murine model
Bibliographic record
Abstract
OBJECTIVE: Aortic macrophage accumulation is characteristic of the pathogenesis of abdominal aortic aneurysm (AAA) but the mechanisms of macrophage accumulation and their phenotype are poorly understood. Lymphatic vessel endothelial receptor-1 (Lyve-1+) resident aortic macrophages independently self-renew and are functionally distinct from monocyte-derived macrophages recruited during inflammation. We hypothesized that Lyve-1+ and Lyve-1- macrophages differentially contribute to aortic aneurysm. Approach and results: Angiotensin-2 and β-aminopropionitrile (AT2/BAPN) were administered to induce AAA in C57BL/6J mice. Using immunohistochemistry (IHC), we demonstrated primarily adventitial accumulation of aortic macrophages, and in association with areas of elastin fragmentation and aortic dissection. Compared with controls, AAA was associated with a relative percent depletion of Lyve-1+ resident aortic macrophages and accumulation of Lyve-1- macrophages. Using CD45.1/CD45.2 parabiosis, we demonstrated aortic macrophage recruitment in AAA. Depletion of aortic macrophages in CCR2-/- mice was associated with reduced aortic dilatation indicating the functional role of recruitment from the bone marrow. Depletion of aortic macrophages using anti-macrophage colony-stimulating factor 1 receptor (MCSF1R)-neutralizing antibody (Ab) reduced the incidence of AAA. Conditional depletion of Lyve-1+ aortic macrophages was achieved by generating Lyve-1wt/cre Csf1rfl/fl mice. Selective depletion of Lyve-1+ aortic macrophages had no protective effects following AT2/BAPN administration and resulted in increased aortic dilatation in the suprarenal aorta. CONCLUSIONS: Aortic macrophage accumulation in AAA derives from adventitial recruitment of Lyve-1- macrophages, with relative percent depletion of Lyve-1+ macrophages. Selective targeting of macrophage subtypes represents a potential novel therapeutic avenue for the medical treatment of AAA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".