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Record W4361293596 · doi:10.1091/mbc.e23-01-0024

Regulation of neurofilament length and transport by a dynamic cycle of phospho-dependent polymer severing and annealing

2023· article· en· W4361293596 on OpenAlexfundno aff
Atsuko Uchida, Juan Peng, Anthony Brown

Bibliographic record

VenueMolecular Biology of the Cell · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeUniversity of California, San DiegoNational Institutes of HealthKing's College LondonUniversité Laval
KeywordsNeurofilamentBiologyPhosphorylationPolymerCell biologyBiophysicsAnnealing (glass)Materials scienceImmunologyComposite material

Abstract

fetched live from OpenAlex

Neurofilaments are cargoes of axonal transport which are unique among known intracellular cargoes in that they are long, flexible protein polymers. These polymers are transported into axons, where they accumulate in large numbers to drive the expansion of axon caliber, which is an important determinant of axonal conduction velocity. We reported previously that neurofilaments can be lengthened by joining ends, called end-to-end annealing, and that they can be shortened by severing. Here, we show that neurofilament annealing and severing are robust and quantifiable phenomena in cultured neurons that act antagonistically to regulate neurofilament length. We show that this in turn regulates neurofilament transport and that severing is regulated by N-terminal phosphorylation of the neurofilament subunit proteins. We propose that focal destabilization of intermediate filaments by site-directed phosphorylation may be a general enzymatic mechanism for severing these cytoskeletal polymers, providing a mechanism to regulate the transport and accumulation of neurofilaments in axons.

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 score0.004
Threshold uncertainty score0.367

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 head0.233
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

Citations10
Published2023
Admission routes1
Has abstractyes

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