Genome-wide identification of phospho-regulators of Wnt signaling in<i>Drosophila</i>
Bibliographic record
Abstract
Evolutionarily conserved intercellular signaling pathways regulate embryonic development and adult tissue homeostasis in metazoans. The precise control of the state and amplitude of signaling pathways is achieved in part through the kinase- and phosphatase-mediated reversible phosphorylation of proteins. In this study, we performed a genome-wide in vivo RNAi screen for kinases and phosphatases that regulate the Wnt pathway under physiological conditions in the Drosophila wing disc. Our analyses have identified 54 high-confidence kinases and phosphatases capable of modulating the Wnt pathway, including 22 novel regulators. These candidates were also assayed for a role in the Notch pathway, and numerous phospho-regulators were identified. Additionally, each regulator of the Wnt pathway was evaluated in the wing disc for its ability to affect the mechanistically similar Hedgehog pathway. We identified 29 dual regulators that have the same effect on the Wnt and Hedgehog pathways. As proof of principle, we established that Cdc37 and Gilgamesh/CK1γ inhibit and promote signaling, respectively, by functioning at analogous levels of these pathways in both Drosophila and mammalian cells. The Wnt and Hedgehog pathways function in tandem in multiple developmental contexts, and the identification of several shared phospho-regulators serve as potential nodes of control under conditions of aberrant signaling and disease.
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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 | 0.000 |
| 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".