Force-induced Myofibroblast Differentiation through Collagen Receptors Is Dependent on Mammalian Diaphanous (mDia)
Bibliographic record
Abstract
The development of fibrosis promotes the differentiation of myofibroblasts, pro-fibrotic cells, which contribute to tissue dysfunction. Myofibroblast differentiation is dependent on actin assembly, which in response to force, is mediated by various actin-binding proteins including the mammalian Diaphanous-related formins (mDia). We examined the role of mDia in the mechano-sensing pathway that leads to force-induced expression of alpha-smooth muscle actin (SMA), a marker and critical determinant of myofibroblast differentiation. In cells treated with siRNA to knockdown mDia and then subjected to tensile force using collagen-coated magnetite beads attached to beta1 integrins, actin assembly was inhibited at bead contact sites. Force-induced nuclear translocation of MRTF-A, a transcriptional co-activator of SMA, was reduced 50% by mDia knockdown. The expression of the transcriptional co-activator of SMA, serum response factor, was reduced by 50% after siRNA knockdown of mDia or by 100% in cells transfected with catalytically inactive mDia. Force-induced activation of the SMA promoter and SMA expression were blocked by knockdown of siRNA of mDia. In anchored collagen gel assays to measure myofibroblast-mediated contraction, knockdown of mDia reduced contraction by 50%. We conclude that mDia plays an important role in the development of force-induced transcriptional activation of SMA and myofibroblast differentiation.
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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".