A249 NEW POTENTIAL ROLE FOR TRANSCRIPTION FACTOR EB IN DNA REPAIR
Bibliographic record
Abstract
We previously demonstrated that prolonged GSK3 inhibition triggers an apoptotic response specifically in pancreatic cancer cells leaving intact pancreatic normal cells. However, we recently observed that the apoptotic response is counterbalanced by pro-survival autophagic signals dependent on the transcription factor EB (TFEB). In order to identify potential mechanisms by which TFEB limits cell death, mass spectrometry analysis was performed to identify new TFEB interacting partners. Many proteins involved in DNA repair were found associated with TFEB upon GSK3 inhibition including PARP1. The aim of this study was to assess whether TFEB participates in DNA damage detection/repair in human pancreatic cancer cells. The experiments were performed using stable population of the pancreatic cancer cells MIA PaCa-2 and PANC1 (PDAC-shCTL) with reduced expression levels of TFEB (PDAC-shTFEB). The specific GSK3 inhibitor CHIR99021 (5uM) was used. Cells were challenged with DNA damaging agents doxorubicin (1uM) or etoposide (10uM). The phosphorylation of H2AX on S139 was evaluated as an indication of DNA damage. 1- GSK3 inhibition induced DNA damage. 2- DNA damaging agents induced DNA damage that were exacerbated in PDAC-shTFEB populations as compared to control PDAC-shCTL populations. 3- TFEB depletion impaired DNA repair upon treatment with DNA damaging agents. 4- PDAC-shTFEB cells were more sensitive to cell death upon GSK3 inhibition or treatment with DNA damaging agents. For the first time, our results provide evidence that TFEB-depleted pancreatic cancer cells are less efficient at repairing DNA damage and are more prone to apoptosis upon treatment with DNA damaging agents. These results suggest that interfering with TFEB function may synergize with GSK3 inhibition and/or DNA damaging agents to promote death of pancreatic cancer cells. CAG, CIHR
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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) | 0.005 | 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".