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Record W2073196507 · doi:10.1083/jcb.200705068

Regulation of Rap1 activity by RapGAP1 controls cell adhesion at the front of chemotaxing cells

2007· article· en· W2073196507 on OpenAlexaff
Taeck J. Jeon, Dai-Jen Lee, Susan Lee, Gerald Weeks, Richard Firtel

Bibliographic record

VenueThe Journal of Cell Biology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical Sciences
KeywordsRap1Cell biologyBiologyCytokinesisChemotaxisActinCell adhesionDictyostelium discoideumCell migrationGuanosineMyosinCell divisionCellReceptorBiochemistrySignal transduction

Abstract

fetched live from OpenAlex

Spatial and temporal regulation of Rap1 is required for proper myosin assembly and cell adhesion during cell migration in Dictyostelium discoideum. Here, we identify a Rap1 guanosine triphosphatase-activating protein (GAP; RapGAP1) that helps mediate cell adhesion by negatively regulating Rap1 at the leading edge. Defects in spatial regulation of the cell attachment at the leading edge in rapGAP1- (null) cells or cells overexpressing RapGAP1 (RapGAP1(OE)) lead to defective chemotaxis. rapGAP1- cells have extended chemoattractant-mediated Rap1 activation kinetics and decreased MyoII assembly, whereas RapGAP1(OE) cells show reciprocal phenotypes. We see that RapGAP1 translocates to the cell cortex in response to chemoattractant stimulation and localizes to the leading edge of chemotaxing cells via an F-actin-dependent pathway. RapGAP1 localization is negatively regulated by Ctx, an F-actin bundling protein that functions during cytokinesis. Loss of Ctx leads to constitutive and uniform RapGAP1 cortical localization. We suggest that RapGAP1 functions in the spatial and temporal regulation of attachment sites through MyoII assembly via regulation of Rap1-guanosine triphosphate.

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)0.001
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 score0.013
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.230
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

Citations81
Published2007
Admission routes1
Has abstractyes

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