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Record W2191156301 · doi:10.1083/jcb.201502036

Ephrin-A3 promotes and maintains slow muscle fiber identity during postnatal development and reinnervation

2015· article· en· W2191156301 on OpenAlexaff
Danny A. Stark, Nathan J. Coffey, Hannah R. Pancoast, Laura L. Arnold, Jacquae Walker, Joanne Vallée, Richard Robitaille, Michael L. Garcia, DDW Cornelison

Bibliographic record

VenueThe Journal of Cell Biology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsUniversité de Montréal
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of Health
KeywordsReinnervationBiologyErythropoietin-producing hepatocellular (Eph) receptorNeuromuscular junctionDenervationMyocyteEphrinCell biologyNeuroscienceAnatomySignal transduction

Abstract

fetched live from OpenAlex

Each adult mammalian skeletal muscle has a unique complement of fast and slow myofibers, reflecting patterns established during development and reinforced via their innervation by fast and slow motor neurons. Existing data support a model of postnatal "matching" whereby predetermined myofiber type identity promotes pruning of inappropriate motor axons, but no molecular mechanism has yet been identified. We present evidence that fiber type-specific repulsive interactions inhibit innervation of slow myofibers by fast motor axons during both postnatal maturation of the neuromuscular junction and myofiber reinnervation after injury. The repulsive guidance ligand ephrin-A3 is expressed only on slow myofibers, whereas its candidate receptor, EphA8, localizes exclusively to fast motor endplates. Adult mice lacking ephrin-A3 have dramatically fewer slow myofibers in fast and mixed muscles, and misexpression of ephrin-A3 on fast myofibers followed by denervation/reinnervation promotes their respecification to a slow phenotype. We therefore conclude that Eph/ephrin interactions guide the fiber type specificity of neuromuscular interactions during development and adult life.

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)0.001
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 score0.017
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.266
Teacher spread0.221 · 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

Citations33
Published2015
Admission routes1
Has abstractyes

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