Poly (ADP-ribose) polymerase 3 (PARP3), a potential repressor of telomerase activity
Bibliographic record
Abstract
BACKGROUND: Considering previous result in Non-Small Cell Lung Cancer (NSCLC), we investigated in human cancer cells the role of PARP3 in the regulation of telomerase activity. METHODS: We selected A549 (lung adenocarcinoma cell line) and Saos-2 (osteosarcoma cell line), with high and low telomerase activity levels, respectively. The first one was transfected using a plasmid construction containing a PARP3 sequence, whereas the Saos-2 cells were submitted to shRNA transfection to get PARP3 depletion. PARP3 expression on both cell systems was evaluated by real-time quantitative PCR and PARP3 protein levels, by Western-blot. Telomerase activity was determined by TRAP assay. RESULTS: In A549 cells, after PARP3 transient transfection, data obtained indicated that twenty-four hours after transfection, up to 100-fold increased gene expression levels were found in the transfected cells with pcDNA/GW-53/PARP3 in comparison to transfected cells with the empty vector. Moreover, 48 hours post-transfection, telomerase activity decreased around 33%, and around 27%, 96 hours post-transfection. Telomerase activity average ratio was 0.67 ± 0.05, and 0.73 ± 0.06, respectively, with significant differences. In Saos-2 cells, after shRNA-mediated PARP3 silencing, a 2.3-fold increase in telomerase activity was detected in relation to the control. CONCLUSION: Our data indicated that, at least in some cancer cells, repression of PARP3 could be responsible for an increased telomerase activity, this fact contributing to telomere maintenance and, therefore, avoiding genome instability.
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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 | 0.001 |
| 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) | 0.002 | 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".