TGFβ‐induced TAZ upregulation and the underlying mechanisms
Bibliographic record
Abstract
TGFβ is a prime inducer of myofibroblast (MF) transition from various precursors. TAZ, a Hippo pathway‐regulated transcriptional coactivator has been shown to crosstalk with TGFβ signaling, primarily as a Smad3 nuclear retention factor. During our studies aimed at assessing the role of TAZ in fibrogenic phenotype shifts, we observed that TGFβ caused a substantial (»5‐fold) and fast (6 h) rise in TAZ protein level in two MF precursors, pericyte‐like (10T1/2) cells and renal fibroblasts. TGFβ increased TAZ in tubular epithelial cells as well, an effect that was strongly potentiated by cell contact uncoupling, a prerequisite for epithelial‐MF transition. Interrogating the underlying signaling in 10T1/2 cells, we found that the Smad3 inhibitor SIS3 failed to abolish TGFβ‐induced TAZ upregulation, whereas inhibition of Akt or p38 abrogated this effect. These findings imply that non‐canonical TGFβ pathways are central to the control of TAZ. While interference with TAZ‐degrading pathways (Casein Kinase δ/ε, GSK3β) increased TAZ expression with similar magnitude and kinetics as TGFβ, cycloheximide‐provoked TAZ degradation was not prevented by TGFβ. Instead, TGFβ increased TAZ mRNA. Since myocardin‐related transcription factor (MRTF) is a TGFβ‐regulated key inducer of MF transition, we treated cells with its inhibitor, CCG‐1423. The drug strongly suppressed TGFβ‐triggered TAZ expression and the concomitant induction of a TAZ‐dependent luciferase reporter. Thus, TGFβ enhances TAZ transcription in an Akt‐, p38‐ and MRTF‐dependent manner, signifying a hitherto under‐recognized TAZ regulatory mechanism with potentially key roles in the pathobiology of organ fibrosis.
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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 | 0.001 |
| 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".