Cell–cell and cell–extracellular matrix adhesions cooperate to organize actomyosin networks and maintain force transmission during dorsal closure
Bibliographic record
Abstract
Tissue morphogenesis relies on the coordinated action of actin networks, cell–cell adhesions, and cell–extracellular matrix (ECM) adhesions. Such coordination can be achieved through cross-talk between cell–cell and cell–ECM adhesions. Drosophila dorsal closure (DC), a morphogenetic process in which an extraembryonic tissue called the amnioserosa contracts and ingresses to close a discontinuity in the dorsal epidermis of the embryo, requires both cell–cell and cell–ECM adhesions. However, whether the functions of these two types of adhesions are coordinated during DC is not known. Here we analyzed possible interdependence between cell–cell and cell–ECM adhesions during DC and its effect on the actomyosin network. We find that loss of cell–ECM adhesion results in aberrant distributions of cadherin-mediated adhesions and actin networks in the amnioserosa and subsequent disruption of myosin recruitment and dynamics. Moreover, loss of cell–cell adhesion caused up-regulation of cell–ECM adhesion, leading to reduced cell deformation and force transmission across amnioserosa cells. Our results show how interdependence between cell–cell and cell–ECM adhesions is important in regulating cell behaviors, force generation, and force transmission critical for tissue morphogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".