Cisplatin Induces Cytotoxicity through the Mitogen-Activated Protein Kinase Pathways ana Activating Transcription Factor 3
Bibliographic record
Abstract
The mechanisms underlying the proapoptotic effect of the chemotherapeutic agent, cisplatin, are largely undefined. Understanding the mechanisms regulating cisplatin cytotoxicity may uncover strategies to enhance the efficacy of this important therapeutic agent. This study evaluates the role of activating transcription factor 3 (ATF3) as a mediator of cisplatin-induced cytotoxicity. Cytotoxic doses of cisplatin and carboplatin treatments consistently induced ATF3 expression in five tumor-derived cell lines. Characterization of this induction revealed a p53, BRCA1, and integrated stress response-independent mechanism, all previously implicated in stress-mediated ATF3 induction. Analysis of mitogen-activated protein kinase (MAPK) pathway involvement in ATF3 induction by cisplatin revealed a MAPK-dependent mechanism. Cisplatin treatment combined with specific inhibitors to each MAPK pathway (c-Jun N-terminal kinase, extracellular signal-regulated kinase, and p38) resulted in decreased ATF3 induction at the protein level. MAPK pathway inhibition led to decreased ATF3 messenger RNA expression and reduced cytotoxic effects of cisplatin as measured by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide cell viability assay. In A549 lung carcinoma cells, targeting ATF3 with specific small hairpin RNA also attenuated the cytotoxic effects of cisplatin. Similarly, ATF3-/- murine embryonic fibroblasts (MEFs) were shown to be less sensitive to cisplatin-induced cytotoxicity compared with ATF3+/+ MEFs. This study identifies cisplatin as a MAPK pathway-dependent inducer of ATF3, whose expression influences cisplatin's cytotoxic effects.
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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".