Increased expression of Mcl‐1 is responsible for the blockage of TRAIL‐induced apoptosis mediated by EGF/ErbB1 signaling pathway
Bibliographic record
Abstract
Epidermal growth factor (EGF) protects against death receptor induced apoptosis in epithelial cells. Herein, we demonstrate that EGF protection against tumor necrosis factor related apoptosis-inducing ligand (TRAIL) induced apoptosis is mediated by increased expression of the Bcl-2 family member myeloid cell leukemia 1 (Mcl-1). EGF increased the mRNA and protein levels of Mcl-1. Furthermore, expression of ErbB1 alone or in combination with ErbB2 in NIH3T3 cells up-regulates Mcl-1 following EGF treatment. In addition, up-regulation of Mcl-1 by EGF is mediated through AKT and NFkappaB activation since kinase inactive AKT and DeltaIkappaB effectively blocks this up-regulation. NFkappaB was also critical for the ability of EGF to prevent TRAIL induced apoptosis as a dominant negative IkappaB (DeltaIkappaB) blocked NFkappaB activation, and relieved EGF protection against TRAIL mediated mitochondrial cytochrome-c release and apoptosis. Finally, anti-sense oligonucleotides directed against Mcl-1 effectively reduced the protein levels of Mcl-1 and blocked EGF protection against TRAIL induced mitochondrial cytochrome-c release and apoptosis. Taken together, EGF signaling leads to increased Mcl-1 expression that is required for blockage of TRAIL induced apoptosis.
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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) | 0.000 | 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".