JNK signaling-mediated exocytosis coordinates epithelial cell-turnover in <i>Drosophila</i> ribosomal protein mutants
Bibliographic record
Abstract
Abstract Robust tissue growth is orchestrated by the precise coordination of cell death and cell proliferation. Our previous study found that in the developing wing pouches of Drosophila Minute /+ animals, both cell death and compensatory cell proliferation are dramatically increased, which contributes to robust growth of mutant tissue. The induction of this cell-turnover depends on activation of JNK signaling, although the mechanism by which JNK activation induces cell-turnover remained unclear. Here, we show that JNK-mediated elevation of exocytosis in dying cells is crucial for triggering cell-turnover in M/+ wing morphogenesis. Mechanistically, elevated JNK signaling in dying cells upregulates exocytosis-related genes and Wingless (Wg), leading to enhanced Wg secretion. Furthermore, this exocytosis-mediated Wg secretion generally occurs downstream of JNK signaling, regardless of the genetic background. Overall, our findings provide mechanistic insights into robust tissue growth through the orchestration of cell-turnover, which is primarily governed by JNK-mediated exocytosis during Drosophila Minute/+ wing morphogenesis.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".