Mechanisms Targeting Apolipoprotein B100 to Proteasomal Degradation
Bibliographic record
Abstract
OBJECTIVE: In lipid-poor states, the ubiquitin-proteasomal pathway rapidly degrades misfolded apolipoprotein B100 (apoB) cotranslationally, although the mechanism of delivery from the ER to cytosolic proteasomes is poorly understood. Here we demonstrate key roles of BiP, an ER luminal chaperone, and p97, a cytosolic ATPase anchored to the ER membrane, in the targeting of apoB for proteasomal degradation. METHODS AND RESULTS: Using coimmunoprecipitations, we observed associations of apoB with BiP, p97, Derlin-1, VIMP, and the E3 ubiquitin ligase Hrd1 in HepG2 cells. BiP and p97 were found to bind apoB cotranslationally. Expression of C-terminal truncated apoB molecules in COS-7 cells showed an N-terminal region outside apoB15 and a C-terminal region found in apoB72 were required for BiP and p97 binding, respectively. Interestingly, overexpression of dominant negative p97 demonstrated that the ATPase activity of p97 was essential for proteasomal degradation of apoB but not for apoB binding. However, p97 activity did not appear to affect the N terminus of apoB, which may be cleaved before degradation. CONCLUSIONS: These data suggest that p97 and BiP play critical roles in the cotranslational delivery of apoB to proteasomes and formation of a degradative complex. Proteasomal degradation appears to selectively target apoB molecules with large C-terminal domains.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".