OxLDL-mediated survival of macrophages does not require LDL internalization or signalling by major pattern recognition receptors
Bibliographic record
Abstract
Macrophages play a key role in the pathogenesis of atherosclerosis, in part by destabilizing plaques. We and others have shown that low concentrations of oxidized LDL (oxLDL) inhibit macrophage apoptosis. As oxLDL is present in lesions, this may be a mechanism by which macrophage populations in the intima are expanded. We have previously shown that oxLDL activates prosurvival signalling pathways such as the phosphoinositide 3-kinase (PI3K) pathway in bone marrow derived macrophages (BMDMs). However, little is known about more upstream signalling events especially at the receptor level. The endocytic pattern recognition receptors (PRRs), scavenger receptor A (SR-A) and CD36, are the main receptors on macrophages for uptake of oxLDL and are therefore important in foam cell formation. The signalling PRRs such as toll-like receptor (TLR) 2 and 4 also bind some types of oxLDL. This study was done to determine if any of the known PRRs are required for the anti-apoptotic effects of oxLDL in BMDMs. To do this, we tested the effect of oxLDL on viability of BMDMs lacking both SR-A and CD36 or lacking TLR2, TLR4, CD14, FcγRIIb, or RAGE. Our results indicate that none of these receptors are essential for activating the oxLDL prosurvival pathway. Furthermore, we show that the anti-apoptotic effect is not dependent on the uptake of oxLDL.
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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 | 0.001 |
| 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".