Mechanical stretch sustains myofibroblast phenotype and function in microtissues through latent TGF-β1 activation
Bibliographic record
Abstract
Abstract Fibrosis is a leading cause of death in developed countries that is characterized by a progressive deterioration of tissue mechanical behavior. Developing methods to study tissue mechanics and myofibroblast activation may lead to new targets for therapeutic treatments that are urgently needed. 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. By 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. Furthermore, these changes were partially reversible upon TGF-β1 withdrawal. In contrast, however, long-term cyclic stretching maintained myofibroblast activation. Furthermore stretching had no effect compared static cultures when TGF-β1 receptors were inhibited and stretching promoted myofibroblast differentiation when given latent TGF-β1. Together these results suggest that external mechanical stretch may activate latent TGF-β1 and 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. Insight box Using a novel high-throughput approach, we quantified the effects of dynamic mechanical stretching on the phenotype and function of cells in 3D microtissue cultures during myofibroblast activation with TGF-β1 treatment and subsequent withdrawal. Our findings show that mechanical stretch may activate endogenously produced latent TGF-β1 to maintain the presence and activity of myofibroblasts after tissue injury. Importantly, through this feed forward mechanism, mechanical stretch might be a powerful stimulus that directs tissues away from recovery and towards the development of 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 | 0.000 |
| 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".