Inhibition of poly (ADP-Ribose) polymerase-1 in telomerase deficient mouse embryonic fibroblasts increases arsenite-induced genome instability
Bibliographic record
Abstract
BACKGROUND: The telomerase enzyme is a viable target for anti-cancer therapy given the innate differences in telomerase activity between tumour cells and normal somatic cells. However, the time lag between telomerase inhibition and telomeres becoming critically short to trigger cell death, allows cancer cells to acquire drug resistance. Inhibition of DNA repair pathways along with telomerase could be an alternative strategy to enhance anti-tumour effects and circumvent the possibility of drug resistance. Poly (ADP-Ribose) Polymerase-1 (PARP-1), an important DNA damage sensor and a DNA repair factor, has important roles in maintaining telomeres and chromosomal stability. In this study, the effects of combined inhibition of PARP-1 and telomerase in mouse embryonic fibroblasts (MEFs) following sodium arsenite exposure (a carcinogen and potent DNA damaging agent), were evaluated. RESULTS: Inhibition of PARP in telomerase deficient MEFs induced an increase in arsenite-induced DNA damage as compared to control cells. Combined inhibition also resulted in enhanced genomic instability, demonstrated by elevated micronuclei induction and chromosomal aberrations with decreased cell survival. In addition, telomerase inhibition in PARP-1 deficient MEFs led to greater telomere shortening and increased genomic instability. CONCLUSIONS: Our study demonstrated that the co-inhibition of PARP-1 and telomerase in MEFs rendered cells more susceptible to DNA damaging agents. Hence, these results offer support for the use of combined inhibition of PARP-1 and telomerase as a strategy to minimise the problems associated with long-term telomerase inhibition in cancer therapeutics.
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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) | 0.001 | 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".