MétaCan
Menu
← Back to cohort
Record W2427184700 · doi:10.1371/journal.pone.0157537

A Farnesyltransferase Acts to Inhibit Ectopic Neurite Formation in C. elegans

2016· article· en· W2427184700 on OpenAlexafffund
David Carr, Leticia Sánchez-Alvarez, Janice H. Imai, Cristina Slatculescu, Nathaniel Noblett, Lei Mao, L.S. Beese, A. Colavita

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaUniversity of OklahomaUniversity of Minnesota
KeywordsNeuritePrenylationCell biologyFarnesyltransferaseBiologyCaenorhabditis elegansMutantMorphogenesisCell polarityCellGeneBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Genetic pathways that regulate nascent neurite formation play a critical role in neuronal morphogenesis. The core planar cell polarity components VANG-1/Van Gogh and PRKL-1/Prickle are involved in blocking inappropriate neurite formation in a subset of motor neurons in C. elegans. A genetic screen for mutants that display supernumerary neurites was performed to identify additional factors involved in this process. This screen identified mutations in fntb-1, the β subunit of farnesyltransferase. We show that fntb-1 is expressed in neurons and acts cell-autonomously to regulate neurite formation. Prickle proteins are known to be post-translationally modified by farnesylation at their C-terminal CAAX motifs. We show that PRKL-1 can be recruited to the plasma membrane in both a CAAX-dependent and CAAX-independent manner but that PRKL-1 can only inhibit neurite formation in a CAAX-dependent manner.

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 score0.003
Threshold uncertainty score0.006

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 integrity0.0000.001
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 head0.024
GPT teacher head0.210
Teacher spread0.186 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

Explore more

Same venuePLoS ONE→Same topicGenetics, Aging, and Longevity in Model Organisms→French-language works237,207→