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Record W2904793147 · doi:10.1111/tra.12629

CLIP‐170 spatially modulates receptor tyrosine kinase recycling to coordinate cell migration

2018· article· en· W2904793147 on OpenAlexafffund
Kossay Zaoui, Stéphanie Duhamel, Christine A. Parachoniak, Morag Park

Bibliographic record

VenueTraffic · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchSusan G. Komen for the Cure
KeywordsCell biologyEndocytic cycleEndosomeReceptor tyrosine kinaseBiologyInternalizationEndocytosisLive cell imagingLamellipodiumTyrosine kinaseIntracellularReceptorCell migrationSignal transductionCellBiochemistry

Abstract

fetched live from OpenAlex

Endocytic sorting of activated receptor tyrosine kinases (RTKs), alternating between recycling and degradative processes, controls signal duration, location and surface complement of RTKs. The microtubule (MT) plus-end tracking proteins (+TIPs) play essential roles in various cellular activities including translocation of intracellular cargo. However, mechanisms through which RTKs recycle back to the plasma membrane following internalization in response to ligand remain poorly understood. We report that net outward-directed movement of endocytic vesicles containing the hepatocyte growth factor (HGF) Met RTK, requires recruitment of the +TIP, CLIP-170, as well as the association of CLIP-170 to MT plus-ends. In response to HGF, entry of Met into Rab4-positive endosomes results in Golgi-localized γ-ear-containing Arf-binding protein 3 (GGA3) and CLIP-170 recruitment to an activated Met RTK complex. We conclude that CLIP-170 co-ordinates the recycling and the transport of Met-positive endocytic vesicles to plus-ends of MTs towards the cell cortex, including the plasma membrane and the lamellipodia, thereby promoting cell migration.

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 score0.087
Threshold uncertainty score0.675

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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

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