Celastrol targets proteostasis and acts synergistically with a heat-shock protein 90 inhibitor to kill human glioblastoma cells
Bibliographic record
Abstract
Glioblastoma multiforme is a devastating disease of the central nervous system and, at present, no effective therapeutic interventions have been identified. Celastrol, a natural occurring triterpene, exhibits potent anti-tumor activity against gliomas in xenograft mouse models. In this study, we describe the cell death mechanism employed by celastrol and identify secondary targets for effective combination therapy against glioblastoma cell survival. In contrast to the previously proposed reactive oxygen species (ROS)-dependent mechanism, cell death in human glioblastoma cells is shown here to be mediated by alternate signal transduction pathways involving, but not fully dependent on, poly(ADP-ribose) polymerase-1 and caspase-3. Our studies indicate that celastrol promotes proteotoxic stress, supported by two feedback mechanisms: (i) impairment of protein quality control as revealed by accumulation of polyubiquitinated aggregates and the canonical autophagy substrate, p62, and (ii) the induction of heat-shock proteins, HSP72 and HSP90. The Michael adduct of celastrol and N-acetylcysteine, 6-N-acetylcysteinyldihydrocelastrol, had no effect on p62, nor on HSP72 expression, confirming a thiol-dependent mechanism. Restriction of protein folding stress with cycloheximide was protective, while combination with autophagy inhibitors did not sensitize cells to celastrol-mediated cytotoxicity. Collectively, these findings imply that celastrol targets proteostasis by disrupting sulfyhydryl homeostasis, independently of ROS, in human glioblastoma cells. This study further emphasizes that targeting proteotoxic stress responses by inhibiting HSP90 with 17-N-Allylamino-17-demethoxygeldanamycin sensitizes human glioblastoma to celastrol treatment, thereby serving as a novel synergism to overcome drug resistance.
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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".