Lipoprotein(a) in atherosclerotic plaques recruits inflammatory cells through interaction with Mac‐1 integrin
Bibliographic record
Abstract
Lipoprotein(a) [Lp(a)], consisting of LDL and the unique constituent apolipoprotein(a) [apo(a)], which contains multiple repeats resembling plasminogen kringle 4, is considered a risk factor for the development of atherosclerotic disorders. However, the underlying mechanisms for the atherogenicity of Lp(a) are not completely understood. Here, we define a novel function of Lp(a) in promoting inflammatory cell recruitment that may contribute to its atherogenicity. Through its apo(a) moiety Lp(a) specifically interacts with the beta2-integrin Mac-1, thereby promoting the adhesion of monocytes and their transendothelial migration in a Mac-1-dependent manner. Interestingly, the interaction between Mac-1 and Lp(a) was strengthened in the presence of proatherogenic homocysteine and was blocked by plasminogen/angiostatin kringle 4. Through its interaction with Mac-1, Lp(a) induced activation of the proinflammatory transcription factor NFkappaB, as well as the NFkappaB-related expression of prothrombotic tissue factor. In atherosclerotic coronary arteries Lp(a) was found to be localized in close proximity to Mac-1 on infiltrating mononuclear cells. Taken together, our data demonstrate that Lp(a), via its apo(a) moiety, is a ligand for the beta2-integrin Mac-1, thereby facilitating inflammatory cell recruitment to atherosclerotic plaques. These observations suggest a novel mechanism for the atherogenic properties of Lp(a).
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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".