Hic-5 is required for myofibroblast differentiation by regulating mechanically dependent MRTF-A nuclear accumulation
Bibliographic record
Abstract
How mechanical cues from the extracellular environment are translated biochemically to modulate the effects of TGF-β on myofibroblast differentiation remains a crucial area of investigation. We report here that the focal adhesion protein, Hic-5 (also known as TGFB1I1), is required for the mechanically dependent generation of stress fibers in response to TGF-β. Successful generation of stress fibers promotes the nuclear localization of the transcriptional co-factor MRTF-A (also known as MKL1), and this correlates with the mechanically dependent induction of α smooth muscle actin (α-SMA) and Hic-5 in response to TGF-β. As a consequence of regulating stress fiber assembly, Hic-5 is required for the nuclear accumulation of MRTF-A and the induction of α-SMA as well as cellular contractility, suggesting a crucial role for Hic-5 in myofibroblast differentiation. Indeed, the expression of Hic-5 was transient in acute wounds and persistent in pathogenic scars, and Hic-5 colocalized with α-SMA expression in vivo. Taken together, these data suggest that a mechanically dependent feed-forward loop, elaborated by the reciprocal regulation of MRTF-A localization by Hic-5 and Hic-5 expression by MRTF-A, plays a crucial role in myofibroblast differentiation in response to TGF-β.
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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 | 0.001 |
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".