Hypomorphic sialidase expression decreases serum cholesterol by downregulation of VLDL production in mice
Bibliographic record
Abstract
Lipoprotein metabolism is an important contributing factor in the development and progression of atherosclerosis. Plasma lipoproteins and their receptors are heavily glycosylated and sialylated, and levels of sialic acids modulate their biological functions. Sialylation is controlled by the activities of sialyltranferases and sialidases. To address the impact of sialidase (neu1) activity on lipoprotein metabolism, we have generated a mouse model with a hypomorphic neu1 allele (B6.SM) that displays reduced sialidase expression and sialidase activity. The objectives of this study are to determine the impact of sialidase on the rate of hepatic lipoprotein secretion and lipoprotein uptake. Our results indicate that hepatic levels of cholesterol and triglycerides are significantly higher in B6.SM mice compared with C57Bl/6 mice; however, VLDL-triglyceride production rate is lower. In addition, B6.SM mice show significantly lower levels of hepatic microsomal triglyceride transfer protein (MTP) and active sterol-regulatory element binding protein (SREBP)-2 but higher levels of diglyceride acyltransferase (DGAT)2; these are all indicative of increased hepatic lipid storage. Rescue of sialidase activity in hypomorphic sialidase mice using helper-dependent adenovirus resulted in increased VLDL production and an increase in MTP levels. Furthermore, hypomorphic sialidase expression results in stabilization of hepatic LDL receptor (LDLR) protein expression, which enhances LDL uptake. These findings provide novel evidence for a central role of sialidase in the cross talk between the uptake and production of lipoproteins.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".