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Record W4289837800 · doi:10.1016/j.jbc.2022.102320

The ARSACS disease protein sacsin controls lysosomal positioning and reformation by regulating microtubule dynamics

2022· article· en· W4289837800 on OpenAlexafffundabout
Vincent Francis, Walaa Alshafie, Rahul Kumar, Martine Girard, Bernard Brais, Peter S. McPherson

Bibliographic record

VenueJournal of Biological Chemistry · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchALS Society of Canada
KeywordsMicrotubuleNeurodegenerationBiologyAxoplasmic transportCell biologySpinocerebellar ataxiaAtaxiaAutophagyPhenotypeGeneGeneticsNeuroscienceMedicinePathologyDisease

Abstract

fetched live from OpenAlex

Autosomal recessive spastic ataxia of Charlevoix-Saguenay is a fatal brain disorder featuring cerebellar neurodegeneration leading to spasticity and ataxia. This disease is caused by mutations in the SACS gene that encodes sacsin, a massive 4579-amino acid protein with multiple modular domains. However, molecular details of the function of sacsin are not clear. Here, using live cell imaging and biochemistry, we demonstrate that sacsin binds to microtubules and regulates microtubule dynamics. Loss of sacsin function in various cell types, including knockdown and KO primary neurons and patient fibroblasts, leads to alterations in lysosomal transport, positioning, function, and reformation following autophagy. Each of these phenotypic changes is consistent with altered microtubule dynamics. We further show the effects of sacsin are mediated at least in part through interactions with JIP3, an adapter for microtubule motors. These data reveal a new function for sacsin that explains its previously reported roles and phenotypes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.005
GPT teacher head0.196
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2022
Admission routes3
Has abstractyes

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