Modulation of sonic hedgehog‐induced mouse embryonic stem cell behaviours through E‐cadherin expression and integrin β1‐dependent F‐actin formation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The sonic hedgehog pathway (Shh) plays a central role in maintaining stem cell function and behaviour in various processes related to self-renewal and tissue regeneration. However, the therapeutic effect of Shh on mouse embryonic stem cells (mESCs) has not yet been clearly elucidated. Thus, we investigated the effect of Shh on the regulation of mESC behaviour as well as the effect of Shh-pretreated mESCs in skin wound healing. EXPERIMENTAL APPROACH: The underlying mechanisms of Shh signalling pathway in growth and motility of mESCs were investigated using Western blot analysis, a cell proliferation assay and cell migration assay. In addition, the effect of Shh-pretreated mESCs in skin wound healing was determined using a mouse excisional wound splinting model. KEY RESULTS: Shh disrupted the adherens junction through proteolysis by activating MMPs. In addition, the release of β-catenin from adherens junctions mediated by Shh led to cell cycle-dependent mESC proliferation. Shh-mediated Gli1 expression led to integrin β1 up-regulation, followed by FAK and Src phosphorylation. Furthermore, among the Rho-GTPases, Rac1 and Cdc42 were activated in a Shh-dependent manner while F-actin expression was suppressed by Rac1 and Cdc42 siRNA transfection. Consistent with the in vitro results, the skin wound healing assay revealed that Shh-treated mESCs increased angiogenesis and skin wound repair compared to that in Shh-treated mESCs transfected with integrin β1 siRNA in vivo. CONCLUSIONS AND IMPLICATIONS: Our results imply that Shh induces adherens junction disruption and integrin β1-dependent F-actin formation by a mechanism involving FAK/Src and Rac1/Cdc42 signalling pathways in mESCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".