Hippo pathway controls cell adhesion and context‐dependent cell competition to influence skin engraftment efficiency
Bibliographic record
Abstract
Cell competition is involved in mammalian embryogenesis and tumor elimination and progression. It was previously shown that, whereas NIH3T3 mouse fibroblasts expressing high levels of the yes-associated protein 1(YAP1) target TEA domain family (TEAD) transcription factors become "winners" in cell competitions, Madin-Darby canine kidney cells expressing activated YAP1 become "losers" and are eliminated from culture monolayers. Thus, YAP1's role in cell competitions is clearly context dependent. Here, we show that keratinocytes overexpressing a constitutively activated YAP1 mutant lose in in vitro competitions with control cells conducted in standard tissue culture dishes and undergo apical extrusion. Similarly, cells in which endogenous YAP1 is activated by NF2 knockdown become losers. The YAP1-overexpressing cells exhibit a decrease in cell-matrix adhesion because of defective expression of adhesion molecules such as fibronectin-1. Cell adhesion-mediated proliferation is also impaired. However, because of intrinsic factors, YAP1-expressing cells proliferate faster than control cells when cocultured in dishes impeding cell adhesion. In vivo, Mob1a/b-deficient (YAP1-activated) epidermis, which shows decreased expression of type XVII collagen, cannot be engrafted successfully onto donor mice. YAP1-activated skin grafts shrink away from surrounding control skin, and the epidermis peels off the basement membrane. Our data show that YAP1 activation controls cell competition in part by decreasing cell adhesion.-Nishio, M., Miyachi, Y., Otani, J., Tane, S., Omori, H., Ueda, F., Togashi, H., Sasaki, T., Mak, T. W., Nakao, K., Fujita, Y., Nishina, H., Maehama, T., Suzuki, A. Hippo pathway controls cell adhesion and context-dependent cell competition to influence skin engraftment efficiency.
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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".