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Record W2052610268 · doi:10.1016/j.celrep.2014.06.019

Arginylation of Myosin Heavy Chain Regulates Skeletal Muscle Strength

2014· article· en· W2052610268 on OpenAlexaff
Anabelle S. Cornachione, Felipe de Souza Leite, Junling Wang, N. Adrian Leu, Albert Kalganov, Denys V. Volgin, Xuemei Han, Tao Xu, Yu‐Shu Cheng, John R R Yates, Dilson E. Rassier, Anna Kashina

Bibliographic record

VenueCell Reports · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcGill University
FundersNational Center for Research ResourcesNational Institute of General Medical Sciences
KeywordsMyosinSkeletal muscleMuscle strengthCell biologyChemistryBiologyBusinessInternal medicineAnatomyMedicine

Abstract

fetched live from OpenAlex

Protein arginylation is a posttranslational modification with an emerging global role in the regulation of actin cytoskeleton. To test the role of arginylation in the skeletal muscle, we generated a mouse model with Ate1 deletion driven by the skeletal muscle-specific creatine kinase (Ckmm) promoter. Ckmm-Ate1 mice were viable and outwardly normal; however, their skeletal muscle strength was significantly reduced in comparison to controls. Mass spectrometry of isolated skeletal myofibrils showed a limited set of proteins, including myosin heavy chain, arginylated on specific sites. Atomic force microscopy measurements of contractile strength in individual myofibrils and isolated myosin filaments from these mice showed a significant reduction of contractile forces, which, in the case of myosin filaments, could be fully rescued by rearginylation with purified Ate1. Our results demonstrate that arginylation regulates force production in muscle and exerts a direct effect on muscle strength through arginylation of myosin.

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 score0.065
Threshold uncertainty score0.374

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations36
Published2014
Admission routes1
Has abstractyes

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