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Record W3045383806 · doi:10.1083/jcb.201901099

HDAC6 regulates microtubule stability and clustering of AChRs at neuromuscular junctions

2020· article· en· W3045383806 on OpenAlexafffund
Alexis Osseni, Aymeric Ravel‐Chapuis, Jean-Luc Thomas, Vincent Gache, Laurent Schaeffer, Bernard J. Jasmin

Bibliographic record

VenueThe Journal of Cell Biology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchCanadian Space AgencyAFM-TéléthonAgence Nationale de la Recherche
KeywordsHDAC6Acetylcholine receptorMicrotubuleCell biologyNeuromuscular junctionPostsynaptic potentialAcetylationChemistryAcetylcholineBiologyReceptorNeuroscienceBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

Microtubules (MTs) are known to be post-translationally modified at the neuromuscular junction (NMJ), hence increasing their stability. To date however, the function(s) of the dynamic MT network and its relative stability in the formation and maintenance of NMJs remain poorly described. Stabilization of the MT is dependent in part on its acetylation status, and HDAC6 is capable of reversing this post-translational modification. Here, we report that HDAC6 preferentially accumulates at NMJs and that it contributes to the organization and the stability of NMJs. Indeed, pharmacological inhibition of HDAC6 protects against MT disorganization and reduces the size of acetylcholine receptor (AChR) clusters. Moreover, the endogenous HDAC6 inhibitor paxillin interacts with HDAC6 in skeletal muscle cells, colocalizes with AChR aggregates, and regulates the formation of AChR. Our findings indicate that the focal insertion of AChRs into the postsynaptic membrane is regulated by stable MTs and highlight how an MT/HDAC6/paxillin axis participates in the regulation of AChR insertion and removal to control the structure of NMJs.

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 score0.011
Threshold uncertainty score0.241

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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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
Published2020
Admission routes2
Has abstractyes

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