Curcumin suppresses multiple DNA damage response pathways and has potency as a sensitizer to PARP inhibitor
Bibliographic record
Abstract
Inhibitors of poly(ADP-ribose) polymerase (PARP) are promising anticancer drugs, particularly for the treatment of tumors deficient in the DNA damage response (DDR). However, it is challenging to design effective therapeutic strategies for use of these compounds against cancers without DDR deficiencies. In this context, combination therapies in which PARP inhibitors are used alongside DDR inhibitors have elicited a great deal of interest. Curcumin, a component of turmeric (Curcuma longa), has been tested in clinical studies for its chemosensitizing potential; however, the mechanisms of chemosensitization by curcumin have not been fully elucidated. This study demonstrates that curcumin suppresses three major DDR pathways: non-homologous end joining (NHEJ), homologous recombination (HR) and the DNA damage checkpoint. Curcumin suppresses the histone acetylation at DNA double-strand break (DSB) sites by inhibiting histone acetyltransferase activity, thereby reducing recruitment of the key NHEJ factor KU70/KU80 to DSB sites. Curcumin also suppresses HR by reducing expression of the BRCA1 gene, which regulates HR, by impairing histone acetylation at the BRCA1 promoter. Curcumin also inhibits ataxia telangiectasia and Rad3-related protein (ATR) kinase (IC50 in vitro = 493 nM), resulting in impaired activation of ATR-CHK1 signaling, which is necessary for HR and the DNA damage checkpoint pathway. Thus, curcumin suppresses three DDR pathways by inhibiting histone acetyltransferases and ATR. Concordantly, curcumin sensitizes cancer cells to PARP inhibitors by enhancing apoptosis and mitotic catastrophe via inhibition of both the DNA damage checkpoint and DSB repair. Our results indicate that curcumin is a promising sensitizer for PARP inhibitor-based therapy.
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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) | 0.003 | 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".