Autophagy is a regulator of TGF-β1-induced fibrogenesis in primary human atrial myofibroblasts
Bibliographic record
Abstract
Transforming growth factor-β(1) (TGF-β(1)) is an important regulator of fibrogenesis in heart disease. In many other cellular systems, TGF-β(1) may also induce autophagy, but a link between its fibrogenic and autophagic effects is unknown. Thus we tested whether or not TGF-β(1)-induced autophagy has a regulatory function on fibrosis in human atrial myofibroblasts (hATMyofbs). Primary hATMyofbs were treated with TGF-β(1) to assess for fibrogenic and autophagic responses. Using immunoblotting, immunofluorescence and transmission electron microscopic analyses, we found that TGF-β(1) promoted collagen type Iα2 and fibronectin synthesis in hATMyofbs and that this was paralleled by an increase in autophagic activation in these cells. Pharmacological inhibition of autophagy by bafilomycin-A1 and 3-methyladenine decreased the fibrotic response in hATMyofb cells. ATG7 knockdown in hATMyofbs and ATG5 knockout (mouse embryonic fibroblast) fibroblasts decreased the fibrotic effect of TGF-β(1) in experimental versus control cells. Furthermore, using a coronary artery ligation model of myocardial infarction in rats, we observed increases in the levels of protein markers of fibrosis, autophagy and Smad2 phosphorylation in whole scar tissue lysates. Immunohistochemistry for LC3β indicated the localization of punctate LC3β with vimentin (a mesenchymal-derived cell marker), ED-A fibronectin and phosphorylated Smad2. These results support the hypothesis that TGF-β(1)-induced autophagy is required for the fibrogenic response in hATMyofbs.
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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".