TGFβ-induced RhoA activation and fibronectin production in mesangial cells require caveolae
Bibliographic record
Abstract
Glomerular sclerosis of diverse etiologies is characterized by mesangial matrix accumulation, with transforming growth factor-beta (TGFbeta) an important pathogenic factor. The GTPase RhoA mediates TGFbeta-induced matrix accumulation in some settings. Here we study the role of the membrane microdomain caveolae in TGFbeta-induced RhoA activation and fibronectin upregulation in mesangial cells (MC). In primary rat MC, TGFbeta1 time dependently increased RhoA and downstream Rho kinase activation. Rho pathway inhibition blocked TGFbeta1-induced upregulation of fibronectin transcript and protein. TGFbeta1-induced RhoA activation was prevented by disrupting caveolae with cholesterol depletion and rescued by cholesterol repletion. Compared with wild types, RhoA/Rho kinase activation was absent in MC lacking caveolae. Reexpression of caveolin-1 (and caveolae) restored these responses. Phosphorylation of caveolin-1 on Y14, effected by Src kinases, has been implicated in signaling responses. Overexpression of nonphosphorylatable caveolin-1 Y14A prevented TGFbeta1-induced RhoA activation. TGFbeta1 also activated Src, and its inhibition blocked RhoA activation. Furthermore, TGFbeta1 led to association of RhoA and caveolin-1. This was prevented by Src or TGFbeta receptor I inhibition, and by caveolin-1 Y14A overexpression. Last, fibronectin upregulation by TGFbeta1 was blocked by Src inhibition, not seen in caveolin-1 knockout MC, and restored by caveolin-1 reexpression in the latter. TGFbeta1-induced collagen I accumulation also required caveolae. TGFbeta1-mediated Smad2/3 activation, however, did not require caveolae. We conclude that RhoA/Rho kinase mediates TGFbeta-induced fibronectin upregulation. This requires caveolae and caveolin-1 interaction with RhoA. Interference with caveolin/caveolae or RhoA signaling thus represents a potential target for the treatment of fibrotic renal disease.
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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".