Pegasus, a small extracellular peptide regulating the short-range diffusion of Wingless
Bibliographic record
Abstract
Abstract Small Open Reading Frames (smORFs) coding for peptides of less than 100 amino-acids are emerging as a fundamental and pervasive gene class, found in the hundreds of thousands in metazoan genomes. Even though some of these genes are annotated, their function, if any, remains unknown. Here we characterize the function of a smORF encoding a short 80 aa peptide, pegasus ( peg ), which facilitates Wg diffusion during the development of the Drosophila wing imaginal disc. During wing development, Wg has sequential functions, and in the later stages, when peg is strongly expressed, it patterns the presumptive wing margin. A reduction in Wg protein secretion at this stage produces effects ranging from total abolition of the wing margin to partial loss of bristles and reduction of proneural gene expression. Here we show that the Peg peptide enhances the short-range of Wg diffusion in this context, in order to produce a proper wing margin. We show that CRISPR/Cas9-mediated mutations of pegasus generate wing margin phenotypes, and changes in target gene expression, consistent with a role in Wg signalling. We find that Peg is secreted, and that it co-localizes and co-immunoprecipitates with Wg, suggesting that Peg directly binds Wg in order to enhance its signalling, and our data from fixed and in-vivo Wg gradient measurements supports a model in which this enhancement occurs by increasing diffusion of extracellular Wg. Our results unveil a new element in the regulation of the Wg signalling pathway, and shed light on the functional consequences of the miss-regulation of Wg diffusion in this developmental context, while also reminding us of the functional diversity, and relevance of small open reading frame genes.
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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".