Surrounding tissue morphogenesis with disrupted posterior midgut invagination during Drosophila gastrulation
Bibliographic record
Abstract
Gastrulation involves multiple, physically-coupled tissue rearrangements. During Drosophila gastrulation, posterior midgut (PMG) invagination promotes both germband extension and hindgut invagination, but whether the normal epithelial rearrangement of PMG invagination is required for morphogenesis of the connected tissues has been unclear. In steppke mutants, epithelial organization of the PMG primordium is strongly disrupted. Despite this disruption, germband extension and hindgut invagination are remarkably effective, and involve myosin network inductions known to promote their wild-type remodelling. Known tissue-autonomous signaling could explain the planar-polarized, junctional myosin networks of the germband, but pushing forces from PMG invagination have been implicated in inducing apical myosin networks of the hindgut primordium. To confirm that the wave of hindgut primordium myosin accumulations is due to mechanical effects, rather than diffusive signalling, we analyzed α-catenin RNAi embryos, in which all of the epithelial tissues initially form but then lose cell-cell adhesion, and observed strongly diminished hindgut primordium myosin accumulations. Thus, alternate mechanical changes in steppke mutants seem to circumvent the lack of normal PMG invagination to induce hindgut myosin networks and invagination. Overall, both germband extension and hindgut invagination are robust to experimental disruption of the PMG invagination, and, although the processes occur with some abnormalities in steppke mutants, there is remarkable redundancy in the multi-tissue system. Such redundancy could allow complex morphogenetic processes to change over evolutionary time.
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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 | 0.001 |
| 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".