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Record W4321485479 · doi:10.1016/j.celrep.2023.112134

Loss of C9orf72 perturbs the Ran-GTPase gradient and nucleocytoplasmic transport, generating compositionally diverse Importin β-1 granules

2023· article· en· W4321485479 on OpenAlexafffund
Philip McGoldrick, Agnes Lau, Zhipeng You, Thomas M. Durcan, Janice Robertson

Bibliographic record

VenueCell Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsCanada Research ChairsMcGill UniversityMontreal Neurological Institute and HospitalOccupational Cancer Research CentreUniversity of Toronto
FundersIrish Motor Neurone Disease AssociationUniversity of California, San DiegoUniversity of TorontoMotor Neurone Disease AssociationFondation Brain CanadaALS Society of Canada
KeywordsC9orf72RanCell biologyImportinNuclear transportNuclear poreBiologyNeurodegenerationTrinucleotide repeat expansionStress granuleChemistryCytoplasmCell nucleusBiochemistryPathologyMedicine

Abstract

fetched live from OpenAlex

repeat expansion in C9orf72 causes amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), eliciting toxic effects through generation of RNA foci, dipeptide repeat proteins, and/or loss of C9orf72 protein. Defects in nucleocytoplasmic transport (NCT) have been implicated as a pathogenic mechanism underlying repeat expansion toxicity. Here, we show that loss of C9orf72 disrupts the Ran-GTPase gradient and NCT in vitro and in vivo. NCT disruption in vivo is enhanced by the presence of compositionally different types of cytoplasmic Importin β-1 granule that exhibit neuronal subtype-specific properties. We show that the abundance of Importin β-1 granules is increased in the context of C9orf72 deficiency, disrupting interactions with nuclear pore complex proteins. These granules appear to associate with the nuclear envelope and are co-immunoreactive for G3BP1 and K63-ubiquitin. These findings link loss of C9orf72 protein to gain-of-function mechanisms and defects in NCT.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.263
Teacher spread0.241 · 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 designObservational
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

Citations24
Published2023
Admission routes2
Has abstractyes

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