Author response: Local mechanical forces promote polarized junctional assembly and axis elongation in Drosophila
Bibliographic record
Abstract
Tissues and organs form certain shapes that allow them to perform particular roles in the body. For example, the lungs form sacs that accommodate large volumes of air, while the skin forms a sheet to cover and protect our internal organs. One way to shape a tissue is for cells to swap places with their neighbours. During this rearrangement, the contacts between neighbouring cells break down before new contacts are formed with other cells. While the physical and molecular signals that guide the break down of cell contacts are well understood, less is known about how new contacts form. Early in development, animal embryos establish a head-to-tail 'axis' that helps to guide where each tissue and organ will form in the body. In fruit fly embryos, the cell rearrangements that drive this process involve cells exchanging places with their neighbours by gathering around a single point. These temporary cell clusters are then organised via new cell contacts that form parallel to the head-to-tail axis. Here, Yu and Fernandez-Gonzalez investigate the role of mechanical forces in forming new cell contacts as the head-tail axis elongates. The experiments show that disrupting the ability of the cells to generate mechanical forces inhibited the formation of new cell contacts and prevented cells from successfully swapping places. Conversely, when mechanical tension is applied at the rearrangement site, the assembly of new cell contacts happens faster. Furthermore, if the tension is applied in different orientations, new cell contacts form parallel to the direction of the mechanical force. Yu and Fernandez-Gonzalez thus show that local mechanical forces direct the assembly of new cell contacts as the head-to-tail axis forms. These forces are most likely generated by cell contractions that appear to create mechanical tension at sites of cell rearrangement. How such physical forces are converted into molecular signals remains a question for future work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.023 |
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".