MétaCan
Menu
Back to cohort
Record W2170229386 · doi:10.1083/jcb.201304174

Microtubule severing by the katanin complex is activated by PPFR-1–dependent MEI-1 dephosphorylation

2013· article· en· W2170229386 on OpenAlexaff
José‐Eduardo Gomes, Nicolas Tavernier, Bénédicte Richaudeau, Étienne Formstecher, Thomas Boulin, Paul E. Mains, Julien Dumont, Lionel Pintard

Bibliographic record

VenueThe Journal of Cell Biology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicrotubuleCell biologyMeiosisMitosisProtein subunitBiologyMeiosis IIGeneticsGene

Abstract

fetched live from OpenAlex

Katanin is an evolutionarily conserved microtubule (MT)-severing complex implicated in multiple aspects of MT dynamics. In Caenorhabditis elegans, the katanin homologue MEI-1 is required for meiosis, but must be inactivated before mitosis. Here we show that PPFR-1, a regulatory subunit of a trimeric protein phosphatase 4 complex, enhanced katanin MT-severing activity during C. elegans meiosis. Loss of ppfr-1, similarly to the inactivation of MT severing, caused a specific defect in meiosis II spindle disassembly. We show that a fraction of PPFR-1 was degraded after meiosis, contributing to katanin inactivation. PPFR-1 interacted with MEL-26, the substrate recognition subunit of the CUL-3 RING E3 ligase (CRL3(MEL-26)), which also targeted MEI-1 for post-meiotic degradation. Reversible protein phosphorylation of MEI-1 may ensure temporal activation of the katanin complex during meiosis, whereas CRL3(MEL-26)-mediated degradation of both MEI-1 and its activator PPFR-1 ensure efficient katanin inactivation in the transition to mitosis.

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 score0.004

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 head0.008
GPT teacher head0.215
Teacher spread0.206 · 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

Citations23
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Cell BiologySame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207