Abstract 315: Regulation of the transcription factor TFEB and the autophagic/lysosomal network by GSK3 in pancreatic cancer cells
Bibliographic record
Abstract
Abstract We recently demonstrated that prolonged inhibition of GSK3 activity induces JNK-dependent cell death in human pancreatic cancer cells. Surprisingly, we observed that, concomitant to apoptosis, GSK3 inhibition also induces autophagy. Preventing this autophagic response sensitizes pancreatic cancer cells to the GSK3 inhibition-induced apoptosis suggesting a protective role for autophagy against cell death caused by prolonged inhibition of GSK3. Nevertheless, the mechanisms underlying the control of autophagy by GSK3 remain unknown. The aim of this study was to determine the molecular mechanism involved in the regulation of autophagy by GSK3 in pancreatic cancer cells. Methods: Experiments were performed in pancreatic cancer cells (PANC1, MIA PaCa2 and BxPC3), and HEK293T cells. GSK3 activity was inhibited using shRNA directed against GSK3beta and/or GSK3alpha or by using the GSK3 specific inhibitors CHIR99021 and SB216763. The JNK inhibitor SP600125 and the mTOR specific inhibitor Torin1 were also used. Results: 1) Inhibition of the JNK-cJun pathway prevented the apoptotic response but not the autophagic response upon GSK3 inhibition excluding a contribution of the JNK-cJUN pathway in the autophagy induced by GSK3 inhibition. We therefore analyzed the activity of the mTOR pathway since this pathway is well known to modulate autophagy. 2) Inhibition of GSK3 activity reduced the activity of S6K1, a direct substrate of the mTOR complex suggesting an inhibition of the mTOR pathway upon GSK3 inhibition. The mTOR pathway was recently shown to regulate autophagy through its impact on the transcription factor TFEB, a master regulator of an autophagic/lysosomal gene network. 3) Interestingly, treatment of cells with GSK3 inhibitors or a mTOR inhibitor both led to the nuclear translocation of TFEB, 4) and correlated with the expression of a lower molecular weight TFEB protein. 5) Pre-treatment of cells with the serine/threonine phosphatase inhibitor calyculin A prevented the molecular weight shift of TFEB induced upon GSK3 inhibition suggesting that GSK3 impacts on TFEB phosphorylation. 6) Furthermore, either GSK3 or mTOR inhibition correlated with increased activity of a TFEB-luciferase reporter gene, 7) and with an increased staining of lysosomes (lysotracker). 8) Interestingly, shRNA-mediated reduction of TFEB expression sensitized pancreatic cancer cells to the apoptosis induced upon GSK3 inhibition. Conclusion: Our results demonstrate that GSK3 inhibition promotes the dephosphorylation of TFEB that correlates with increased transcriptional activity of this master regulator of autophagy and lysosomal biogenesis. Furthermore, our results support a protective role against cell death assumed by TFEB upon GSK3 inhibition. Citation Format: Benoît Marchand, Alexandre Raymond-Fleury, Marie-Josée Boucher. Regulation of the transcription factor TFEB and the autophagic/lysosomal network by GSK3 in pancreatic cancer cells. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 315. doi:10.1158/1538-7445.AM2014-315
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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".