Myosin II Controls Junction Fluctuations to Guide Epithelial Tissue Ordering
Bibliographic record
Abstract
Abstract Homophilic interactions between E-Cadherin molecules generate adhesive interfaces or junctions (AJs) that connect neighbouring cells in epithelial monolayers. These are highly dynamic structures. Under conditions of homeostasis, changes in the length of individual interfaces provide epithelia with the fluidity required to maintain tissue integrity in the face of cell division, delamination and extrinsic forces. Furthermore, when acted upon by polarized actomyosin-based forces, changes in AJ length can also drive neighbour exchange to reshape an entire tissue. Whilst the contribution of AJ remodelling to developmental morphogenesis has been subjected to intensive study, less is known about AJ dynamics in other circumstances. Here, using a combination of experiment and computational modelling, we study AJ dynamics in an epithelium that undergoes a gradual increase in packing order without concomitant large-scale changes in tissue shape or size. Under these conditions, we find that neighbour exchange events are driven by stochastic fluctuations in junction length, which are regulated at least in part by the level of junctional actomyosin. As a result of this behaviour, the steady increase in junctional actomyosin and consequent tension that accompanies development steadily reduces the rate of neighbour exchange and orders the tissue. This leads us to propose a model in which topological transitions, that underpin tissue fluidity, are either inhibited or biased by actomyosin-based forces, to drive, respectively, tissue ordering or deformation.
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".