Myofibroblast YAP/TAZ activation is a key step in organ fibrogenesis
Bibliographic record
Abstract
Fibrotic diseases account for nearly half of all deaths in the developed world. Despite its importance, the pathogenesis of fibrosis remains poorly understood. Recently, the two mechanosensitive transcription cofactors YAP and TAZ have emerged as important profibrotic regulators in multiple murine tissues. Despite this growing recognition, a number of important questions remain unanswered, including which cell types require YAP/TAZ activation for fibrosis to occur and the time course of this activation. Here, we present a detailed analysis of the role that myofibroblast YAP and TAZ play in organ fibrosis and the kinetics of their activation. Using analyses of cells, as well as multiple murine and human tissues, we demonstrated that myofibroblast YAP and TAZ were activated early after organ injury and that this activation was sustained. We further demonstrated the critical importance of myofibroblast YAP/TAZ in driving progressive scarring in the kidney, lung, and liver, using multiple transgenic models in which YAP and TAZ were either deleted or hyperactivated. Taken together, these data establish the importance of early injury-induced myofibroblast YAP and TAZ activation as a key event driving fibrosis in multiple organs. This information should help guide the development of new antifibrotic YAP/TAZ inhibition strategies.
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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".