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Record W2345681059 · doi:10.1101/gad.279745.116

Diverging roles for Lrp4 and Wnt signaling in neuromuscular synapse development during evolution

2016· article· en· W2345681059 on OpenAlexfundno aff
Leonor Remédio, Katherine D. Gribble, Jennifer K. Lee, Natalie Kim, Peter T. Hallock, Nicolas Delestrée, George Z. Mentis, Robert C. Froemke, Michael Granato, Steven J. Burden

Bibliographic record

VenueGenes & Development · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Neurological Disorders and StrokeYork University
KeywordsAgrinBiologyFrizzledZebrafishNeuromuscular junctionSynapseCell biologyWnt signaling pathwayNeuroscienceCongenital myasthenic syndromeAcetylcholine receptorSignal transductionReceptorGeneticsGene

Abstract

fetched live from OpenAlex

Motor axons approach muscles that are prepatterned in the prospective synaptic region. In mice, prepatterning of acetylcholine receptors requires Lrp4, a LDLR family member, and MuSK, a receptor tyrosine kinase. Lrp4 can bind and stimulate MuSK, strongly suggesting that association between Lrp4 and MuSK, independent of additional ligands, initiates prepatterning in mice. In zebrafish, Wnts, which bind the Frizzled (Fz)-like domain in MuSK, are required for prepatterning, suggesting that Wnts may contribute to prepatterning and neuromuscular development in mammals. We show that prepatterning in mice requires Lrp4 but not the MuSK Fz-like domain. In contrast, prepatterning in zebrafish requires the MuSK Fz-like domain but not Lrp4. Despite these differences, neuromuscular synapse formation in zebrafish and mice share similar mechanisms, requiring Lrp4, MuSK, and neuronal Agrin but not the MuSK Fz-like domain or Wnt production from muscle. Our findings demonstrate that evolutionary divergent mechanisms establish muscle prepatterning in zebrafish and mice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.249
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 teacher head, 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

Citations59
Published2016
Admission routes1
Has abstractyes

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