Autophagy modulates temozolomide-induced cell death in alveolar Rhabdomyosarcoma cells
Bibliographic record
Abstract
Rhabdomyosarcoma (RMS) is a muscle-derived tumor. In both pre-clinical and clinical studies Temozolomide (TMZ) has been recently tested against RMS; however, the precise mechanism of action of TMZ in RMS remains unclear. Here we demonstrate that TMZ decreases the cell viability of the RH30 RMS and C2C12 cell line, where cells display evidence of mitochondrial outer membrane permeability. Interestingly, the C2C12 mouse myoblast line was relatively more resistant to TMZ-induced apoptosis. Moreover, we observed that TMZ activated biochemical and morphological markers of autophagy in both cell lines. Autophagy inhibition in both RH30 and C2C12 cells significantly increased TMZ-induced cell death. In RH30 cells, TMZ increased Mcl-1 and Bax protein expression compared to corresponding time match controls while in C2C12 Mcl-1, Bcl-2, Bcl-XL, and Bax protein expression were not changed. Baf-A1 co-treatment with TMZ significantly decrease Mcl-1 expression compared to TMZ while increase Bax expression in C2C12 cells (Bcl2 and Bcl-XL do not significantly change in Baf-A1/TMZ co-treatment). Using a three-dimensional (3D) C2C12 and RH30 culture model we demonstrated that TMZ is significantly more toxic in RH30 cells (live/dead assay). Additionally, we have observed in our 3D culture model that TMZ induced both apoptosis (cleavage of PARP) and autophagy (LC3-puncta and localization of LC3/p62). Therefore, our data demonstrate that TMZ induces simultaneous autophagy and apoptosis in both RH30 and C2C12 cells in 2D and 3D culture model, where RH30 cells are more sensitive to TMZ-induced death. Furthermore, autophagy serves to protect RH30 cells from TMZ-induced death.
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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".