Function and molecular regulation of WNT‐5A expression by TGF‐β
Bibliographic record
Abstract
Disruption of WNT‐5A homeostasis has been linked to various disorders like fibrosis, cancer and inflammation with recent evidence implicating it in asthma. Here, we have investigated the function and molecular regulation of WNT‐5A expression in airway smooth muscle (ASM) cells by TGF‐β. Immortalized human ASM cells were used to measure mRNA by qRT‐PCR and protein abundance by immunoblotting. ASM cells expressed a variety of WNT ligands and Frizzled receptors which were differentially modulated by TGF‐β. WNT‐5A was the most abundant ligand expressed and showed ~4–16 fold upregulation in response to TGF‐β (2 ng/mL). Knockdown using siRNA demonstrated WNT‐5A requirement for TGF‐β‐induced collagen and fibronectin expression. TGF‐β induction of WNT‐5A appeared to require TGF‐β activated kinase (TAK)‐1 activity, as pharmacological inhibition of TAK‐1 with LL‐Z1640–2 (500 nM) prevented WNT5A expression. Interestingly, inhibition of ERK1/2 (U0126, 3μM), Smad3 (SIS3, 3μM) and β‐catenin/TCF (PKF115–584, 100 nM) augmented WNT‐5A expression suggesting a negative regulatory role. In conclusion, TGF‐β‐induced WNT‐5A expression mediates extracellular matrix expression by human ASM cells. WNT5A induction occurs via complex signaling cascades with TAK‐1 acting as a positive regulator, whereas ERK1/2, Smad3 and β‐catenin/TCF mediate a negative constraint. This study is funded by University of Groningen.
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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 | 0.001 | 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".