Elastin-derived peptides potentiate atherosclerosis through the immune Neu1–PI3Kγ pathway
Bibliographic record
Abstract
AIMS: Elastin is degraded during vascular ageing and its products, elastin-derived peptides (EP), are present in the human blood circulation. EP binds to the elastin receptor complex (ERC) at the cell surface, composed of elastin-binding protein (EBP), a cathepsin A and a neuraminidase 1. Some in vitro functions have clearly been attributed to this binding, but the in vivo implications for arterial diseases have never been clearly investigated. METHODS AND RESULTS: Here, we demonstrate that chronic doses of EP injected into mouse models of atherosclerosis increase atherosclerotic plaque size formation. Similar effects were observed following an injection of a VGVAPG peptide, suggesting that the ERC mediates these effects. The absence of phosphoinositide 3-kinase γ (PI3Kγ) in bone marrow-derived cells prevented EP-induced atherosclerosis development, demonstrating that PI3Kγ drive EP-induced arterial lesions. Accordingly, in vitro studies showed that PI3Kγ was required for EP-induced monocyte migration and ROS production and that this effect was dependent upon neuraminidase activity. Finally, we showed that degradation of elastic lamellae in LDLR(-/-) mice fed an atherogenic diet correlated with atherosclerotic plaque formation. At the same time, the absence of the cathepsin A-neuraminidase 1 complex in cells of the haematopoietic lineage abolished atheroma plaque size progression and decreased leucocytes infiltration, clearly demonstrating the role of this complex in atherogenesis and suggesting the involvement of endogenous EP. CONCLUSION: Altogether, this work identifies EP as an enhancer of atherogenesis and defines the Neuraminidase 1/PI3Kγ signalling pathway as a key mediator of this function in vitro and in vivo.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".