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Record W3204788202 · doi:10.1038/s41467-021-25785-z

Pegasus, a small extracellular peptide enhancing short-range diffusion of Wingless

2021· article· en· W3204788202 on OpenAlexaff
Emile G. Magny, Ana Isabel Platero, Sarah A. Bishop, José Ignacio Pueyo, Daniel Aguilar‐Hidalgo, Juan Pablo Couso

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersBiotechnology and Biological Sciences Research Council
KeywordsExtracellularPeptideRange (aeronautics)DiffusionCell biologyComputational biologyBiologyBiochemistryPhysicsMaterials science

Abstract

fetched live from OpenAlex

Small Open Reading Frames (smORFs) coding for peptides of less than 100 amino-acids are an enigmatic and pervasive gene class, found in the tens of thousands in metazoan genomes. Here we reveal a short 80 amino-acid peptide (Pegasus) which enhances Wingless/Wnt1 protein short-range diffusion and signalling. During Drosophila wing development, Wingless has sequential functions, including late induction of proneural gene expression and wing margin development. Pegasus mutants produce wing margin defects and proneural expression loss similar to those of Wingless. Pegasus is secreted, and co-localizes and co-immunoprecipitates with Wingless, suggesting their physical interaction. Finally, measurements of fixed and in-vivo Wingless gradients support that Pegasus increases Wingless diffusion in order to enhance its signalling. Our results unveil a new element in Wingless signalling and clarify the patterning role of Wingless diffusion, while corroborating the link between small open reading frame peptides, and regulation of known proteins with membrane-related functions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.301
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2021
Admission routes1
Has abstractyes

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