Regulation of GST-MDA-7 toxicity in human glioblastoma cells by ERBB1, ERK1/2, PI3K, and JNK1-3 pathway signaling
Bibliographic record
Abstract
The present studies defined the biological effects of a GST fusion protein of melanoma differentiation-associated gene-7 (mda-7), GST-MDA-7 (1 and 30 nmol/L), on cell survival and cell signaling in primary human glioma cells in vitro. GST-MDA-7, in a dose- and time-dependent fashion killed glioma cells with diverse genetic characteristics; 1 nmol/L caused arrest without death, whereas 30 nmol/L caused arrest and killing after exposure. Combined inhibition of extracellular signal-regulated kinase 1/2 (ERK1/2) and AKT function was required to enhance 1 nmol/L GST-MDA-7 lethality in all cell types, whereas combined activation of MEK1 and AKT was required to suppress 30 nmol/L GST-MDA-7 lethality; both effects are mediated in part by modulating c-Jun NH(2)-terminal kinase (JNK) 1-3 activity. The geldanamycin 17AAG inhibited AKT and ERK1/2 in GBM cells and enhanced GST-MDA-7 lethality. JNK1-3 signaling promoted BAX activation and mitochondrial dysfunction. In GBM6 cells, GST-MDA-7 (30 nmol/L) transiently activated p38 mitogen-activated protein kinase, which was modestly protective against JNK1-3-induced toxicity, whereas GST-MDA-7 (300 nmol/L) caused prolonged intense p38 mitogen-activated protein kinase activation, which promoted cell death. In GBM12 cells that express full-length mutant activated ERBB1, inhibition of ERBB1 did not modify GST-MDA-7 lethality; however, in U118 established glioma cells, stable overexpression of wild-type ERBB1 and/or truncated active ERBB1vIII suppressed GST-MDA-7 lethality. Our data argue that combined inhibition of ERK1/2 and AKT function, regardless of genetic background, promotes MDA-7 lethality in human primary human glioma cells via JNK1-3 signaling and is likely to represent a more ubiquitous approach to enhancing MDA-7 toxicity in this cell type than inhibition of ERBB1 function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".