Overexpressed Gliotactin activates BMP signaling through interfering with the Tkv–Dad association
Bibliographic record
Abstract
Epithelial junctions ensure cell–cell adhesion and establish permeability barriers between cells. At the corners of epithelia, the tricellular junction (TCJ) is formed by three adjacent epithelial cells and generates a functional barrier. In Drosophila, a key TCJ protein is Gliotactin (Gli) where loss of Gli disrupts barrier formation and function. Conversely, overexpressed Gli spreads away from the TCJ and triggers apoptosis, delamination, and cell migration. Thus, Gli protein levels are tightly regulated and by two mechanisms, at the protein levels by tyrosine phosphorylation and endocytosis and at the mRNA level through microRNA-184. Regulation of Gli mRNA is mediated through a Gli–BMP–miR184 feedback loop. Excessive Gli triggers BMP signaling pathway through the activation of Tkv type-I BMP receptor and Mad. Elevated level of pMad induces micrRNA-184 expression which in turn targets the Gli 3′UTR and mRNA degradation. Gli activation of Tkv is not through its ligand Dpp but rather through the inhibition of Dad, an inhibitory-Smad. Here, we show that ectopic expression of Gli interferes with Tkv–Dad association by sequestering Dad away from Tkv. The reduced inhibitory effect of Dad on Tkv results in the increased Tkv–pMad signaling activity, and this effect is continuous through larval and pupal wing formation.
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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) | 0.002 | 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".