Data from Garcinol Potentiates TRAIL-Induced Apoptosis through Modulation of Death Receptors and Antiapoptotic Proteins
Bibliographic record
Abstract
Abstract Whether garcinol, the active component of Garcinia indica, can modulate the sensitivity of cancer cells to TRAIL, a cytokine currently in phase II clinical trial, was investigated. We found that garcinol potentiated TRAIL-induced apoptosis of cancer cells as indicated by intracellular esterase activity, DNA strand breaks, accumulation of the membrane phospholipid phosphatidylserine, mitochondrial activity, and activation of caspase-8, -9, and -3. We found that garcinol, independent of the cell type, induced both of the TRAIL receptors, death receptor 4 (DR4) and DR5. Garcinol neither induced the receptors on normal cells nor sensitized them to TRAIL. Deletion of DR5 or DR4 by small interfering RNA significantly reduced the apoptosis induced by TRAIL and garcinol. In addition, garcinol downregulated various cell survival proteins including survivin, bcl-2, XIAP, and cFLIP, and induced bid cleavage, bax, and cytochrome c release. Induction of death receptors by garcinol was found to be independent of modulation of CCAAT/enhancer-binding protein-homologous protein, p53, bax, extracellular signal-regulated kinase, or c-Jun-NH2-kinase. The effect of garcinol was mediated through the generation of reactive oxygen species, in as much as induction of both death receptors, modulation of antiapoptotic and proapoptotic proteins, and potentiation of TRAIL-induced apoptosis were abolished by N-acetyl cysteine and glutathione. Interestingly, garcinol also converted TRAIL-resistant cells into TRAIL-sensitive cells. Overall, our results indicate that garcinol can potentiate TRAIL-induced apoptosis through upregulation of death receptors and downregulation of antiapoptotic proteins. Mol Cancer Ther; 9(4); 856–68. ©2010 AACR.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".