Calmodulin inhibition contributes to sensitize TRAIL-induced apoptosis in human lung cancer H1299 cells
Bibliographic record
Abstract
Tumor necrosis factor related apoptosis-inducing ligand (TRAIL) preferentially triggers apoptosis in tumor cells versus normal cells. However, TRAIL alone is not effective in treating TRAIL-resistant tumors. We evaluated the effect of 180 enzyme inhibitors on TRAIL-induced apoptosis in human lung cancer H1299 cells, and found fluphenazine-N-2-chloroethane (a calmodulin (CaM) antagonist) sensitized TRAIL-induced apoptosis. Interestingly, in the presence of TRAIL, it increased caspase-8 binding to the Fas-associated death domain (FADD), but decreased binding of FADD-like interleukin-1beta-converting enzyme inhibitory proteins (FLIPs). Additionally, its combination with TRAIL inhibited Akt phosphorylation. These results were consistently observed in cells treated with CaM siRNA. We suggested the blockade of CaM could sensitize lung cancer cells to TRAIL-induced apoptosis in at least 2 ways: (i) it can activate death-inducing signaling complex mediated apoptosis by inhibiting TRAIL-induced binding of FLIP and TRAIL-enhanced binding of caspase-8 to FADD; (ii) it can inhibit Akt phosphorylation, consequently leading to decreased expression of anti-apoptotic molecules such as FLIP and members of the inhibitor of apoptosis protein family. This study suggests the combination of CaM antagonists with TRAIL may have the therapeutic potential to overcome the resistance of lung cancers to 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) | 0.002 | 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".