Activation of ATM/Chk1 by curcumin causes cell cycle arrest and apoptosis in human pancreatic cancer cells
Bibliographic record
Abstract
Curcumin has been shown to inhibit the growth of various types of cancer cells; however, at concentrations much above the clinically achievable levels in humans. The concentration of curcumin achieved in the plasma after oral administration in humans was estimated to be around 1.8 microM. Here, we report that treatment of BxPC-3 human pancreatic cancer cells with a low and single exposure of 2.5 microM curcumin for 24 h causes significant arrest of cells in the G2/M phase and induces significant apoptosis. Immunoblot studies revealed increased phosphorylation of H2A.X at Ser-139 and Chk1 at Ser-280 and a decrease in DNA polymerase-beta level in curcumin-treated cells. Phosphorylation of H2A.X and Chk1 proteins are an indicator of DNA damage whereas DNA polymerase-beta plays a role in the repair of DNA strand breaks. Normal immortalised human pancreatic ductal epithelial (HPDE-6) cells remained unaffected by curcumin treatment. In addition, we also observed a significant increase in the phosphorylation of Chk1 at Ser-345, Cdc25C at Ser-216 and a subtle increase in ATM phosphorylation at Ser-1981. Concomitant decrease in the expressions of cyclin B1 and Cdk1 were seen in curcumin-treated cells. Further, curcumin treatment caused significant cleavage of caspase-3 and PARP in BxPC-3 but not in HPDE-6 cells. Silencing ATM/Chk1 expression by transfecting BxPC-3 cells with ATM or Chk1-specific SiRNA blocked the phosphorylation of ATM, Chk1 and Cdc25C and protected the cells from curcumin-mediated G2/M arrest and apoptosis. This study reflects the critical role of ATM/Chk1 in curcumin-mediated G2/M cell cycle arrest and apoptosis in pancreatic cancer cells.
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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 | 0.001 |
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".