Abstract 432: MEK/ERK Inhibition Corrects the Defect in VLDL Assembly and Secretion in HepG2 Cells via Activation of Cell Death--Inducing DFFA-Like Effector B (Cide B), ApoCIII and Lipin-1
Bibliographic record
Abstract
Suppression of MEK/ERK signalling in HepG2 cells promotes the assembly and secretion of VLDL-sized apoB100-containing lipoproteins via an unknown mechanism (Tsai et al, Arterioscler Thromb Vasc Biol 2007; 27:211-218). In this report, we investigated the mechanisms underlying the profound stimulatory effects of MEK/ERK inhibition on VLDL assembly and secretion in HepG2 cells. Following MEK/ERK inhibition with the inhibitor U0126, the mRNA levels of lipin-1α, -1β, apoCIII and CideB were significantly increased by 1.22, 1.25, 3.6, and 1.9 fold, respectively, all key factors involved in intracellular lipid metabolism. Gain-of-function studies were then used to mimic the effects seen with MEK/ERK inhibition. Upon transient overexpression of lipin-1α, and-1β, secretion of VLDL-apoB was increased by 2.07 and 2.23 fold, respectively. Similarly, overexperession of wild type apoCIII (C3WT) significantly increased the secretion of VLDL-apoB. By contrast, loss-of-function approaches using RNA interference showed that knockdown of apoCIII or CideB significantly decreased secretion of VLDL-apoB by 22% or 69%, respectively. Overexpression of MTP could not block the inhibitory effects of CideB knockdown on VLDL secretion in MEK/ERK-inhibited HepG2 cells. Taken together, our data suggest that increased expression of a number of key protein factors, particularly CideB, may mediate the profound stimulatory effect of MEK/ERK inhibition on VLDL assembly and secretion. Enhanced expression of CideB, apo CIII, and lipin-1 may alter and promote availability of TG pools available for VLDL assembly in a process independent of MTP.
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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) | 0.003 | 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".