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Record W2963557796 · doi:10.15252/embr.201847495

FAM83D directs protein kinase CK1α to the mitotic spindle for proper spindle positioning

2019· article· en· W2963557796 on OpenAlexafffund
Luke J. Fulcher, Zhengcheng He, Lin Mei, Thomas Macartney, Nicola T. Wood, Alan R. Prescott, Arlene Whigham, Joby Varghese, Robert Gourlay, Graeme Ball, Rosemary G. Clarke, David G. Campbell, Christopher A. Maxwell, Gopal P. Sapkota

Bibliographic record

VenueEMBO Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersMedical Research CouncilCanadian Institutes of Health ResearchUK Research and InnovationUniversity of DundeeGovernment of CanadaGlaxoSmithKline
KeywordsLibrary scienceComputer science

Abstract

fetched live from OpenAlex

Abstract The concerted action of many protein kinases helps orchestrate the error‐free progression through mitosis of mammalian cells. The roles and regulation of some prominent mitotic kinases, such as cyclin‐dependent kinases, are well established. However, these and other known mitotic kinases alone cannot account for the extent of protein phosphorylation that has been reported during mammalian mitosis. Here we demonstrate that CK1α, of the casein kinase 1 family of protein kinases, localises to the spindle and is required for proper spindle positioning and timely cell division. CK1α is recruited to the spindle by FAM83D, and cells devoid of FAM83D , or those harbouring CK1α‐binding‐deficient FAM83D F283A/F283A knockin mutations, display pronounced spindle positioning defects, and a prolonged mitosis. Restoring FAM83D at the endogenous locus in FAM83D − / − cells, or artificially delivering CK1α to the spindle in FAM83D F283A/F283A cells, rescues these defects. These findings implicate CK1α as new mitotic kinase that orchestrates the kinetics and orientation of cell division.

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 score0.136
Threshold uncertainty score0.483

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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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

Citations62
Published2019
Admission routes2
Has abstractyes

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