Monocyte subsets differentially employ CCR2, CCR5, and CX3CR1 to accumulate within atherosclerotic plaques
Bibliographic record
Abstract
Monocytes participate critically in atherosclerosis. There are 2 major subsets expressing different chemokine receptor patterns: CCR2(+)CX3CR1(+)Ly-6C(hi) and CCR2(-)CX3CR1(++)Ly-6C(lo) monocytes. Both C-C motif chemokine receptor 2 (CCR2) and C-X(3)-C motif chemokine receptor 1 (CX3CR1) are linked to progression of atherosclerotic plaques. Here, we analyzed mouse monocyte subsets in apoE-deficient mice and traced their differentiation and chemokine receptor usage as they accumulated within atherosclerotic plaques. Blood monocyte counts were elevated in apoE(-/-) mice and skewed toward an increased frequency of CCR2(+)Ly-6C(hi) monocytes in apoE(-/-) mice fed a high-fat diet. CCR2(+)Ly-6C(hi) monocytes efficiently accumulated in plaques, whereas CCR2(-)Ly-6C(lo) monocytes entered less frequently but were more prone to developing into plaque cells expressing the dendritic cell-associated marker CD11c, indicating that phagocyte heterogeneity in plaques is linked to distinct types of entering monocytes. CCR2(-) monocytes did not rely on CX3CR1 to enter plaques. Instead, they were partially dependent upon CCR5, which they selectively upregulated in apoE(-/-) mice. By comparison, CCR2(+)Ly-6C(hi) monocytes unexpectedly required CX3CR1 in addition to CCR2 and CCR5 to accumulate within plaques. In many other inflammatory settings, these monocytes utilize CCR2, but not CX3CR1, for trafficking. Thus, antagonizing CX3CR1 may be effective therapeutically in ameliorating CCR2(+) monocyte recruitment to plaques without impairing their CCR2-dependent responses to inflammation overall.
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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) | 0.001 | 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".