Assessing methods to quantitatively validate TGFβ-dependent autophagy
Bibliographic record
Abstract
Transforming growth factor beta (TGFβ) promotes tumorigenesis by suppressing immune surveillance and inducing epithelial to mesenchymal transition (EMT). TGFβ may augment tumorigenesis by activating autophagy, which protects cancer cells from chemotherapy and promotes invasive and anti-apoptotic properties. Here, we assess how TGFβ1 modulates autophagy related (ATG) gene expression and ATG protein levels. We also assessed microtubule-associated protein light chain 3 (LC3) lipidation, LC3 puncta formation and autophagosome-lysosome co-localization in non-small cell lung cancer (NSCLC) cell lines. These experimental approaches were validated using pharmacological autophagy inhibitors (chloroquine and spautin-1) and an autophagy activator (MG132). We found that TGFβ1, chloroquine and MG132 had little effect on ATG protein levels but increased LC3 lipidation, LC3 puncta formation and autophagosome-lysosome co-localization. Since similar outcomes were observed using chloroquine and MG132, we concluded that several techniques employed to assess TGFβ-dependent autophagy may not differentiate between the activation of autophagy vs. lysosomal inhibition. Thus, NSCLC cell lines stably expressing a GFP-LC3-RFP-LC3ΔG autophagic flux probe were used to assess TGFβ-mediated autophagy. Using this approach, we observed that TGFβ, MG132 and serum starvation increased autophagic flux, whereas chloroquine and spautin-1 decreased autophagic flux. Finally, we demonstrated that ATG5 and ATG7 are critical for TGFβ-dependent autophagy in NSCLC cells. The application of this model will fuel future experiments to characterize TGFβ-dependent autophagy, which is necessary to understand the molecular processes that link, TGFβ, autophagy and tumorigenesis.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".