Mechanical stretch sustains myofibroblast phenotype and function in microtissues through latent TGF-β1 activation
Why this work is in the frame
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Bibliographic record
Abstract
Developing methods to study tissue mechanics and myofibroblast activation may lead to new targets for therapeutic treatments that are urgently needed for fibrotic disease. Microtissue arrays are a promising approach to conduct relatively high-throughput research into fibrosis as they recapitulate key biomechanical aspects of the disease through a relevant 3D extracellular environment. In early work, our group developed a device called the MVAS-force to stretch microtissues while enabling simultaneous assessment of their dynamic mechanical behavior. Here, we investigated TGF-β1-induced fibroblast to myofibroblast differentiation in microtissue cultures using our MVAS-force device through assessing α-SMA expression, contractility and stiffness. In doing so, we linked cell-level phenotypic changes to functional changes that characterize the clinical manifestation of fibrotic disease. As expected, TGF-β1 treatment promoted a myofibroblastic phenotype and microtissues became stiffer and possessed increased contractility. These changes were partially reversible upon TGF-β1 withdrawal under a static condition, while, in contrast, long-term cyclic stretching maintained myofibroblast activation. This pro-fibrotic effect of mechanical stretching was absent when TGF-β1 receptors were inhibited. Furthermore, stretching promoted myofibroblast differentiation when microtissues were given latent TGF-β1. Altogether, these results suggest that external mechanical stretch may activate latent TGF-β1 and, accordingly, might be a powerful stimulus for continued myofibroblast activation to progress fibrosis. Further exploration of this pathway with our approach may yield new insights into myofibroblast activation and more effective therapeutic treatments for fibrosis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it