Neuraminidases 1 and 3 Trigger Atherosclerosis by Desialylating Low‐Density Lipoproteins and Increasing Their Uptake by Macrophages
Bibliographic record
Abstract
Background Chronic vascular disease atherosclerosis starts with an uptake of atherogenic modified low‐density lipoproteins (LDLs) by resident macrophages, resulting in formation of arterial fatty streaks and eventually atheromatous plaques. Increased plasma sialic acid levels, increased neuraminidase activity, and reduced sialic acid LDL content have been previously associated with atherosclerosis and coronary artery disease in human patients, but the mechanism underlying this association has not been explored. Methods and Results We tested the hypothesis that neuraminidases contribute to development of atherosclerosis by removing sialic acid residues from glycan chains of the LDL glycoprotein and glycolipids. Atherosclerosis progression was investigated in apolipoprotein E and LDL receptor knockout mice with genetic deficiency of neuraminidases 1, 3, and 4 or those treated with specific neuraminidase inhibitors. We show that desialylation of the LDL glycoprotein, apolipoprotein B 100, by human neuraminidases 1 and 3 increases the uptake of human LDL by human cultured macrophages and by macrophages in aortic root lesions in Apoe −/− mice via asialoglycoprotein receptor 1. Genetic inactivation or pharmacological inhibition of neuraminidases 1 and 3 significantly delays formation of fatty streaks in the aortic root without affecting the plasma cholesterol and LDL levels in Apoe −/− and Ldlr −/− mouse models of atherosclerosis. Conclusions Together, our results suggest that neuraminidases 1 and 3 trigger the initial phase of atherosclerosis and formation of aortic fatty streaks by desialylating LDL and increasing their uptake by resident macrophages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".