TGF-β-Activated Kinase 1 (TAK1) Signaling Regulates TGF-β-Induced WNT-5A Expression in Airway Smooth Muscle Cells via Sp1 and β-Catenin
Bibliographic record
Abstract
WNT-5A, a key player in embryonic development and post-natal homeostasis, has been associated with a myriad of pathological conditions including malignant, fibroproliferative and inflammatory disorders. Previously, we have identified WNT-5A as a transcriptional target of TGF-β in airway smooth muscle cells and demonstrated its function as a mediator of airway remodeling. Here, we investigated the molecular mechanisms underlying TGF-β-induced WNT-5A expression. We show that TGF-β-activated kinase 1 (TAK1) is a critical mediator of WNT-5A expression as its pharmacological inhibition or siRNA-mediated silencing reduced TGF-β induction of WNT-5A. Furthermore, we show that TAK1 engages p38 and c-Jun N-terminal kinase (JNK) signaling which redundantly participates in WNT-5A induction as only simultaneous, but not individual, inhibition of p38 and JNK suppressed TGF-β-induced WNT-5A expression. Remarkably, we demonstrate a central role of β-catenin in TGF-β-induced WNT-5A expression. Regulated by TAK1, β-catenin is required for WNT-5A induction as its silencing repressed WNT-5A expression whereas a constitutively active mutant augmented basal WNT-5A abundance. Furthermore, we identify Sp1 as the transcription factor for WNT-5A and demonstrate its interaction with β-catenin. We discover that Sp1 is recruited to the WNT-5A promoter in a TGF-β-induced and TAK1-regulated manner. Collectively, our findings describe a TAK1-dependent, β-catenin- and Sp1-mediated signaling cascade activated downstream of TGF-β which regulates WNT-5A induction.
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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 | 0.001 |
| 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 | 0.001 |
| 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".