Autophagy fosters myofibroblast differentiation through MTORC2 activation and downstream upregulation of CTGF
Bibliographic record
Abstract
Recent evidence suggests that autophagy may favor fibrosis through enhanced differentiation of fibroblasts in myofibroblasts. Here, we sought to characterize the mediators and signaling pathways implicated in autophagy-induced myofibroblast differentiation. Fibroblasts, serum starved for up to 4 d, showed increased LC3-II/-I ratios and decreased SQSTM1/p62 levels. Autophagy was associated with acquisition of markers of myofibroblast differentiation including increased protein levels of ACTA2/αSMA (actin, α 2, smooth muscle, aorta), enhanced gene and protein levels of COL1A1 (collagen, type I, α 1) and COL3A1, and the formation of stress fibers. Inhibiting autophagy with 3 different class I phosphoinositide 3-kinase and class III phosphatidylinositol 3-kinase (PtdIns3K) inhibitors or through ATG7 silencing prevented myofibroblast differentiation. Autophagic fibroblasts showed increased expression and secretion of CTGF (connective tissue growth factor), and CTGF silencing prevented myofibroblast differentiation. Phosphorylation of the MTORC1 target RPS6KB1/p70S6K kinase was abolished in starved fibroblasts. Phosphorylation of AKT at Ser473, a MTORC2 target, was reduced after initiation of starvation but was followed by spontaneous rephosphorylation after 2 d of starvation, suggesting the reactivation of MTORC2 with sustained autophagy. Inhibiting MTORC2 activation with long-term exposure to rapamycin or by silencing RICTOR, a central component of the MTORC2 complex abolished AKT rephosphorylation. Both RICTOR silencing and rapamycin treatment prevented CTGF and ACTA2 upregulation, demonstrating the central role of MTORC2 activation in CTGF induction and myofibroblast differentiation. Finally, inhibition of autophagy with PtdIns3K inhibitors or ATG7 silencing blocked AKT rephosphorylation. Collectively, these results identify autophagy as a novel activator of MTORC2 signaling leading to CTGF induction and myofibroblast differentiation.
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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) | 0.001 | 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 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".