Canonical and Non-canonical TGFβ Signaling Activate Autophagy in an ULK1-Dependent Manner
Bibliographic record
Abstract
The mechanism(s) in which transforming growth factor beta 1 (TGFβ) modulates autophagy in cancer remain unclear. Here, we characterized the TGFβ signaling pathways that induce autophagy in non-small cell lung cancer cells, using cells lines stably expressing GFP-LC3-RFP-LC3ΔG constructs that measure autophagic flux. We demonstrated that TGFβ1 increases Unc 51-like kinase 1 (ULK1) protein levels, 5' adenosine monophosphate-activated protein kinase (AMPK)-dependent ULK1 phosphorylation at serine (S) 555 and ULK1 complex formation but decreases mechanistic target of rapamycin (mTOR) activity on ULK1. Further analysis revealed that the canonical Smad4 pathway and the non-canonical TGFβ activated kinase 1/tumor necrosis factor receptor-associated factor 6/P38 mitogen activated protein kinase (TAK1-TRAF6-P38 MAPK) pathway are important for TGFβ1-induced autophagy. The TAK1-TRAF6-P38 MAPK pathway was essential for downregulating mTOR S2448 phosphorylation, ULK1 S555 phosphorylation and autophagosome formation. Furthermore, although siRNA-mediated Smad4 silencing did not alter mTOR-dependent ULK1 S757 phosphorylation, it did reduce AMPK-dependent ULK1 S555 phosphorylation and autophagosome formation. Additionally, Smad4 silencing and inhibiting the TAK1-TRAF6-P38 MAPK pathway decreased autophagosome-lysosome co-localization in the presence of TGFβ. Our results suggest that the Smad4 and TAK1-TRAF6-P38 MAPK signaling pathways are essential for TGFβ-induced autophagy and provide specific targets for the inhibition of TGFβ in tumor cells that utilize autophagy in their epithelial-mesenchymal transition program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".