Sialidase down-regulation reduces non-HDL cholesterol, inhibits leukocyte transmigration, and attenuates atherosclerosis in ApoE knockout mice
Bibliographic record
Abstract
Atherosclerosis is a complex disease that involves alterations in lipoprotein metabolism and inflammation. Protein and lipid glycosylation events, such as sialylation, contribute to the development of atherosclerosis and are regulated by specific glycosidases, including sialidases. To evaluate the effect of the sialidase neuraminidase 1 (NEU1) on atherogenesis, here we generated apolipoprotein E (ApoE)-deficient mice that express hypomorphic levels of NEU1 ( Neu1 hypo Apoe −/− ). We found that the hypomorphic NEU1 expression in male Apoe −/− mice reduces serum levels of very-low-density lipoprotein (VLDL) and LDL cholesterol, diminishes infiltration of inflammatory cells into lesions, and decreases aortic sinus atherosclerosis. Transplantation of Apoe −/− bone marrow (BM) into Neu1 hypo Apoe −/− mice significantly increased atherosclerotic lesion development and had no effect on serum lipoprotein levels. Moreover, Neu1 hypo Apoe −/− mice exhibited a reduction in circulating monocyte and neutrophil levels and had reduced hyaluronic acid and P-selectin adhesion capability on monocytes/neutrophils and T cells. Consistent with these findings, administration of a sialidase inhibitor, 2-deoxy-2,3-dehydro- N -acetylneuraminic acid, had a significant anti-atherogenic effect in the Apoe −/− mice. In summary, the reduction in NEU1 expression or function decreases atherosclerosis in mice via its significant effects on lipid metabolism and inflammatory processes. We conclude that NEU1 may represent a promising target for managing atherosclerosis.
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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".