Targeting p53 via JNK Pathway: A Novel Role of RITA for Apoptotic Signaling in Multiple Myeloma
Bibliographic record
Abstract
The low frequency of p53 alterations e.g., mutations/deletions (∼10%) in multiple myeloma (MM) makes this tumor type an ideal candidate for p53-targeted therapies. RITA is a small molecule which can induce apoptosis in tumor cells by activating the p53 pathway. We previously showed that RITA strongly activates p53 while selectively inhibiting growth of MM cells without inducing genotoxicity, indicating its potential as a drug lead for p53-targeted therapy in MM. However, the molecular mechanisms underlying the pro-apoptotic effect of RITA are largely undefined. Gene expression analysis by microarray identified a significant number of differentially expressed genes associated with stress response including c-Jun N-terminal kinase (JNK) signaling pathway. By Western blot analysis we further confirmed that RITA induced activation of p53 in conjunction with up-regulation of phosphorylated ASK-1, MKK-4 and c-Jun. These results suggest that RITA induced the activation of JNK signaling. Chromatin immunoprecipitation (ChIP) analysis showed that activated c-Jun binds to the activator protein-1 (AP-1) binding site of the p53 promoter region. Disruption of the JNK signal pathway by small interfering RNA (siRNA) against JNK or JNK specific inhibitor, SP-600125 inhibited the activation of p53 and attenuated apoptosis induced by RITA in myeloma cells carrying wild type p53. On the other hand, p53 transcriptional inhibitor, PFT-α or p53 siRNA not only inhibited the activation of p53 transcriptional targets but also blocked the activation of c-Jun suggesting the presence of a positive feedback loop between p53 and JNK. In addition, RITA in combination with dexamethasone, known as a JNK activator, displays synergistic cytotoxic responses in MM cell lines and patient samples. Our study unveils a previously undescribed mechanism of RITA-induced p53-mediated apoptosis through JNK signaling pathway and provides the rationale for combination of p53 activating drugs with JNK activators in the treatment of MM.
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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) | 0.001 | 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".