Huh-7 or HepG2 cells: which is the better model for studying human apolipoprotein-B100 assembly and secretion?
Bibliographic record
Abstract
Apolipoprotein-B100 (apoB100) is the essential protein for the assembly and secretion of very low density lipoproteins (VLDL) from liver. The hepatoma HepG2 cell line has been the cell line of choice for the study of synthesis and secretion of human apoB-100. Despite the general use of HepG2 cells to study apoB100 metabolism, they secrete relatively dense, lipid-poor particles compared with VLDL secreted in vivo. Recently, Huh-7 cells were adopted as an alternative model to HepG2 cells, with the implicit assumption that Huh-7 cells were superior in some respects of lipoprotein metabolism, including VLDL secretion. In this study we addressed the hypothesis that the spectrum of apoB100 lipoprotein particles secreted by Huh-7 cells more closely resembles the native state in human liver. We find that Huh-7 cells resemble HepG2 cells in the effects of exogenous lipids, microsomal triglyceride transfer protein (MTP)-inhibition, and proteasome inhibitors of apoB100 secretion, recovery, and degradation. In contrast to HepG2 cells, however, MEK-ERK inhibition does not correct the defect in VLDL secretion. Huh-7 cells do not appear to offer any advantages over HepG2 cells as a general model of human apoB100-lipoprotein metabolism.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".