Mechanisms of glucosamine-induced suppression of the hepatic assembly and secretion of apolipoprotein B-100-containing lipoproteins
Bibliographic record
Abstract
Glucosamine-induced endoplasmic reticulum (ER) stress was recently shown to specifically reduce apolipoprotein B-100 (apoB-100) secretion by enhancing the proteasomal degradation of apoB-100. Here, we examined the mechanisms linking glucosamine-induced ER stress and apoB-lipoprotein biogenesis. Trypsin sensitivity studies suggested glucosamine-induced changes in apoB-100 conformation. Endoglycosidase H studies of newly synthesized apoB-100 revealed glucosamine induced N-linked glycosylation defects resulting in reduced apoB-100 secretion. We also examined glucosamine-induced changes in VLDL assembly and secretion. A dose-dependent (1-10 mM glucosamine) reduction was observed in VLDL-apoB-100 secretion in primary hepatocytes (24.2-67.3%) and rat McA-RH7777 cells (23.2-89.5%). Glucosamine also inhibited the assembly of larger VLDL-, LDL-, and intermediate density lipoprotein-apoB-100 but did not affect smaller HDL-sized apoB-100 particles. Glucosamine treatment during the chase period (posttranslational) led to a 24% reduction in apoB-100 secretion (P < 0.01; n = 4) and promoted post-ER apoB degradation. However, the contribution of post-ER apoB-100 degradation appeared to be quantitatively minor. Interestingly, the glucosamine-induced posttranslational reduction in apoB-100 secretion could be partially prevented by treatment with desferrioxamine or vitamin E. Together, these data suggest that cotranslational glucosamine treatment may cause defects in apoB-100 N-linked glycosylation and folding, resulting in enhanced proteasomal degradation. Posttranslationally, glucosamine may interfere with the assembly process of apoB lipoproteins, leading to post-ER degradation via nonproteasomal pathways.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".