Rapamycin and UCN-01 synergistically induce apoptosis in human leukemia cells through a process that is regulated by the Raf-1/MEK/ERK, Akt, and JNK signal transduction pathways
Bibliographic record
Abstract
Interactions between the protein kinase C and Chk1 inhibitor UCN-01 and rapamycin in human leukemia cells have been investigated in relation to apoptosis induction. Treatment of U937 monocytic leukemia cells with rapamycin (10 nmol/L) in conjunction with a minimally toxic concentration of UCN-01 (100 nmol/L) for 36 hours resulted in marked potentiation of mitochondrial injury (i.e., loss of mitochondrial membrane potential and cytosolic release of cytochrome c, AIF, and Smac/DIABLO), caspase activation, and apoptosis. The release of cytochrome c, AIF, and Smac/DIABLO were inhibited by BOC-D-fmk, indicating that their release was caspase dependent. These events were associated with marked down-regulation of Raf-1, MEK, and ERK phosphorylation, diminished Akt activation, and enhanced phosphorylation of c-Jun NH2-terminal kinase (JNK). Coadministration of UCN-01 and rapamycin reduced the expression levels of the antiapoptotic members of the Bcl-2 family Mcl-1 and Bcl-xL and diminished the expression of cyclin D1 and p34(cdc2). Furthermore, enforced expression of a constitutively active MEK1 or, to a lesser extent, myristoylated Akt construct partially but significantly attenuated UCN-01/rapamycin-mediated lethality in both U937 and Jurkat cell systems. Finally, inhibition of the stress-related JNK by SP600125 or by the expression of a dominant-negative mutant of c-Jun significantly attenuated apoptosis induced by rapamycin/UCN-01. Together, these findings indicate that the mammalian target of rapamycin inhibitor potentiates UCN-01 cytotoxicity in a variety of human leukemia cell types and suggest that inhibition of both Raf-1/MEK/ERK and Akt cytoprotective signaling pathways as well as JNK activation contribute to this phenomenon.
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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".