TGF‐β1 induces EMT reprogramming of porcine bladder urothelial cells into collagen producing fibroblasts‐like cells in a Smad2/Smad3‐dependent manner
Bibliographic record
Abstract
Activation of fibroblasts and their differentiation into myofibroblasts, excessive collagen production and fibrosis occurs in a number of bladder diseases. Similarly, conversion of epithelial cells into mesenchymal cells (EMT) has been shown to increase fibroblasts like cells. TGF-β1 can induce the EMT and the role of TGF-β1-induced EMT during bladder injury leading to fibrosis and possible organ failure is gaining increasing interest. Here we show that EMT and fibrosis in porcine bladder urothelial (UC) cells are Smad dependent. Fresh normal porcine bladder urothelial cells were grown in culture with or without TGF-β1 and EMT markers were assessed. TGF-β1 treatment induced changes in cellular morphology as depicted by a significant decrease in the expression of E-cadherin and corresponding increase in N-cadherin and α-SMA. We knocked down Smad2 and Smad3 by Smad specific siRNA. Downregulation of E-cadherin expression by TGF-β1 was Smad3-dependent, whereas N-cadherin and α-SMA were dependent on both Smad2 and Smad3. Connective tissue growth factor (CTGF/CCN2), matrix metalloproteinase-2 and -9 (MMP-2, MMP-9) has been shown to play important roles in the pathogenesis of fibrosis. Induction of these genes by TGF-β1 was found to be time dependent. Upregulation of CTGF/CCN2 by TGF-β1 was Smad3 dependent; whereas MMP-2 was Smad2 dependent. Smad2 and Smad3 both participated in MMP-9 expression. TGF-β1 reprogrammed mesenchymal fibroblast like cells robustly expressed collagen I and III and these was inhibited by SB-431542, a TGF-β receptor inhibitor. Our results indicate that EMT of porcine bladder UC cells is TGF-β1 dependent and is mediated through Smad2 and Smad3. TGF-β1 may be an important factor in the development of bladder fibrosis via an EMT mechanism. This identifies a potential amenable therapeutic target.
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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) | 0.002 | 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".