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Record W2021351330 · doi:10.1083/jcb.200804114

Phosphorylation regulates targeting of cytoplasmic dynein to kinetochores during mitosis

2008· article· en· W2021351330 on OpenAlexaff
Jacqueline Whyte, Jason R. Bader, Sinji Borges Ferreira Tauhata, Maurice Raycroft, Jessica E. Hornick, K. Kevin Pfister, William S. Lane, Gordon K. Chan, Edward H. Hinchcliffe, Patricia S. Vaughan, Kevin T. Vaughan

Bibliographic record

VenueThe Journal of Cell Biology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsUniversity of Alberta
FundersNational Institute of General Medical Sciences
KeywordsDynactinKinetochoreDyneinCell biologyBiologyMicrotubuleMitosisDynein ATPaseMetaphaseGeneticsChromosome

Abstract

fetched live from OpenAlex

Cytoplasmic dynein functions at several sites during mitosis; however, the basis of targeting to each site remains unclear. Tandem mass spectrometry analysis of mitotic dynein revealed a phosphorylation site in the dynein intermediate chains (ICs) that mediates binding to kinetochores. IC phosphorylation directs binding to zw10 rather than dynactin, and this interaction is needed for kinetochore dynein localization. Phosphodynein associates with kinetochores from nuclear envelope breakdown to metaphase, but bioriented microtubule (MT) attachment and chromosome alignment induce IC dephosphorylation. IC dephosphorylation stimulates binding to dynactin and poleward streaming. MT depolymerization, release of kinetochore tension, and a PP1-gamma mutant each inhibited IC dephosphorylation, leading to the retention of phosphodynein at kinetochores and reduced poleward streaming. The depletion of kinetochore dynactin by moderate levels of p50(dynamitin) expression disrupted the ability of dynein to remove checkpoint proteins by streaming at metaphase but not other aspects of kinetochore dynein activity. Together, these results suggest a new model for localization of kinetochore dynein and the contribution of kinetochore dynactin.

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 score0.003
Threshold uncertainty score0.288

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 head0.005
GPT teacher head0.210
Teacher spread0.204 · 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

Citations121
Published2008
Admission routes1
Has abstractyes

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