Targeting Unc5b in macrophages drives atherosclerosis regression and pro-resolving immune cell function
Bibliographic record
Abstract
Atherosclerosis results from lipid-driven inflammation of the arterial wall that fails to resolve. Imbalances in macrophage accumulation and function, including diminished migratory capacity and defective efferocytosis, fuel maladaptive inflammation and plaque progression. The neuroimmune guidance cue netrin-1 has dichotomous roles in inflammation partly due to its multiple receptors; in atherosclerosis, netrin-1 promotes macrophage survival and retention via its receptor Unc5b. To minimize the pleiotropic effects of targeting netrin-1, we tested the therapeutic potential of deleting Unc5b in mice with advanced atherosclerosis. We generated Unc5b fl/fl Cx3cr1 creERT2/WT mice, which allowed conditional deletion of Un5b (∆ Unc5b MØ ) in monocytes and macrophages by tamoxifen injection. After inducing advanced atherosclerosis by hepatic PCSK9 overexpression and western diet feeding for 20 wk, Unc5b was deleted and hypercholesterolemia was normalized to simulate clinical lipid management. Deletion of myeloid Unc5b led to a 40% decrease in atherosclerotic plaque burden and reduced plaque complexity compared to Unc5b fl/fl Cx3cr1 WT/WT littermate controls (Ctrl MØ ). Consistently, plaque macrophage content was reduced by 50% in ∆ Unc5b MØ mice due to reduced plaque Ly6C hi monocyte recruitment and macrophage retention. Compared to Ctrl MØ mice, plaques in ∆ Unc5b MØ mice had reduced necrotic area and fewer apoptotic cells, which correlated with improved efferocytotic capacity by Unc5b -deficient macrophages in vivo and in vitro. Beneficial changes in macrophage dynamics in the plaque upon Unc5b deletion were accompanied by an increase in atheroprotective T cell populations, including T-regulatory and Th2 cells. Our data identify Unc5b in advanced atherosclerosis as a therapeutic target to induce pro-resolving restructuring of the plaque immune cells and to promote atherosclerosis regression.
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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 | 0.001 | 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".