MétaCan
Menu
Back to cohort
Record W2341829254 · doi:10.1091/mbc.e15-09-0627

Abnormal intermediate filament organization alters mitochondrial motility in giant axonal neuropathy fibroblasts

2015· article· en· W2341829254 on OpenAlexaff
Jason Lowery, Nikhil Jain, Edward R. Kuczmarski, Saleemulla Mahammad, Anne E. Goldman, Vladimir I. Gelfand, Puneet Opal, Robert D. Goldman

Bibliographic record

VenueMolecular Biology of the Cell · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsMcMaster University
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institutes of Health
KeywordsMotilityBiologyCell biologyMitochondrionOrganelleIntermediate filamentCellCytoskeletonBiochemistry

Abstract

fetched live from OpenAlex

Giant axonal neuropathy (GAN) is a rare disease caused by mutations in the GAN gene, which encodes gigaxonin, an E3 ligase adapter that targets intermediate filament (IF) proteins for degradation in numerous cell types, including neurons and fibroblasts. The cellular hallmark of GAN pathology is the formation of large aggregates and bundles of IFs. In this study, we show that both the distribution and motility of mitochondria are altered in GAN fibroblasts and this is attributable to their association with vimentin IF aggregates and bundles. Transient expression of wild-type gigaxonin in GAN fibroblasts reduces the number of IF aggregates and bundles, restoring mitochondrial motility. Conversely, silencing the expression of gigaxonin in control fibroblasts leads to changes in IF organization similar to that of GAN patient fibroblasts and a coincident loss of mitochondrial motility. The inhibition of mitochondrial motility in GAN fibroblasts is not due to a global inhibition of organelle translocation, as lysosome motility is normal. Our findings demonstrate that it is the pathological changes in IF organization that cause the loss of mitochondrial motility.

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 score0.017
Threshold uncertainty score0.574

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 science0.0010.001
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 head0.009
GPT teacher head0.240
Teacher spread0.232 · 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

Citations37
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMolecular Biology of the CellSame topicSkin and Cellular Biology ResearchFrench-language works237,207