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Record W2142750219 · doi:10.1242/jcs.01611

RNA association and nucleocytoplasmic shuttling by ataxin-1

2004· article· en· W2142750219 on OpenAlexaff
Stuart Irwin, Mark F. van Delft, Deborah Pinchev, Jenny L. Howell, Joanna Graczyk, Harry T. Orr, Ray Truant

Bibliographic record

VenueJournal of Cell Science · 2004
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsMcMaster University
FundersNational Institute of Neurological Disorders and Stroke
KeywordsPolyglutamine tractBiologyCell biologyRNASpinocerebellar ataxiaNuclear proteinMutantCell nucleusNuclear transportMessenger RNANuclear localization sequenceNucleusRNA-binding proteinTranscription (linguistics)Transcription factorGeneticsGeneHuntingtin

Abstract

fetched live from OpenAlex

Spinocerebellar ataxia type 1 (SCA1) is a dominant neurodegenerative disease caused by the expression of mutant ataxin-1 containing an expanded polyglutamine tract. Ataxin-1 is a nuclear protein that localizes to punctate inclusions similar to neuronal nuclear inclusions seen in many polyglutamine expansion disease proteins. We demonstrate that ataxin-1 localization to inclusions and inclusion dynamics within the nucleus are RNA and transcription dependent, but not dependent on the polyglutamine tract. Ataxin-1 nuclear inclusions are distinct from other described nuclear bodies but recruit the mRNA export factor, TAP/NXF1, in a manner that is enhanced by cell heat shock. By FRAP protein dynamic studies in live cells, we found that wild-type, but not mutant, ataxin-1 was capable of nuclear export. These results suggest that the normal role of ataxin-1 may be in RNA processing, perhaps nuclear RNA export. Thus, nuclear retention of mutant ataxin-1 may be an important toxic gain of function in SCA1 disease.

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)0.0000.000
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)0.0010.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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

Citations118
Published2004
Admission routes1
Has abstractyes

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Same venueJournal of Cell ScienceSame topicGenetic Neurodegenerative DiseasesFrench-language works237,207