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Record W3024579974 · doi:10.3390/cells9051238

Functional Characterization of Neurofilament Light Splicing and Misbalance in Zebrafish

2020· article· en· W3024579974 on OpenAlexafffund
Doris Lou Demy, María Letizia Campanari, Raphael Munoz‐Ruiz, Heather D. Durham, Benoît J. Gentil, Edor Kabashi

Bibliographic record

VenueCells · 2020
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchAssociation pour la Recherche sur la Sclérose Latérale Amyotrophique et autres Maladies du Motoneurone
KeywordsNeurofilamentZebrafishRNA splicingAlternative splicingBiologyGene isoformCell biologyProtein subunitTARDBPAxonAmyotrophic lateral sclerosisRNA-binding proteinNeuroscienceMessenger RNAGeneRNAGeneticsSOD1MutantMedicinePathology

Abstract

fetched live from OpenAlex

Neurofilaments (NFs), a major cytoskeletal component of motor neurons, play a key role in the differentiation, establishment and maintenance of their morphology and mechanical strength. The de novo assembly of these neuronal intermediate filaments requires the presence of the neurofilament light subunit (NEFL), whose expression is reduced in motor neurons in amyotrophic lateral sclerosis (ALS). This study used zebrafish as a model to characterize the NEFL homologue neflb, which encodes two different isoforms via a splicing of the primary transcript (neflbE4 and neflbE3). In vivo imaging showed that neflb is crucial for proper neuronal development, and that disrupting the balance between its two isoforms specifically affects the NF assembly and motor axon growth, with resultant motor deficits. This equilibrium is also disrupted upon the partial depletion of TDP-43 (TAR DNA-binding protein 43), an RNA-binding protein encoded by the gene TARDBP that is mislocalized into cytoplasmic inclusions in ALS. The study supports the interaction of the NEFL expression and splicing with TDP-43 in a common pathway, both biologically and pathogenetically.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.189

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 head0.027
GPT teacher head0.235
Teacher spread0.209 · 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
Published2020
Admission routes2
Has abstractyes

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