Conformational changes of apoB-100 in SMase-modified LDL mediate formation of large aggregates at acidic pH
Bibliographic record
Abstract
During atherogenesis, the extracellular pH of atherosclerotic lesions decreases. Here, we examined the effect of low, but physiologically plausible pH on aggregation of modified LDL, one of the key processes in atherogenesis. LDL was treated with SMase, and aggregation of the SMase-treated LDL was followed at pH 5.5-7.5. The lower the pH, the more extensive was the aggregation of identically prelipolyzed LDL particles. At pH 5.5-6.0, the aggregates were much larger (size >1 µm) than those formed at neutral pH (100-200 nm). SMase treatment was found to lead to a dramatic decrease in α-helix and concomitant increase in β-sheet structures of apoB-100. Particle aggregation was caused by interactions between newly exposed segments of apoB-100. LDL-derived lipid microemulsions lacking apoB-100 failed to form large aggregates. SMase-induced LDL aggregation could be blocked by lowering the incubation temperature to 15°C, which also inhibited the changes in the conformation of apoB-100, by proteolytic degradation of apoB-100 after SMase-treatment, and by HDL particles. Taken together, sphingomyelin hydrolysis induces exposure of protease-sensitive sites of apoB-100, whose interactions govern subsequent particle aggregation. The supersized LDL aggregates may contribute to the retention of LDL lipids in acidic areas of atherosclerosis-susceptible sites in the arterial intima.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".