MétaCan
Menu
Back to cohort
Record W2439691210

ARF1 and ARF6 are dispensable for Crk-dependent epithelial-mesenchymal-like transitions.

2003· article· en· W2439691210 on OpenAlexaff
Louie Lamorte, Morag Park

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsAdapter molecule crkAdherens junctionSignal transducing adaptor proteinCell biologyHepatocyte growth factorPaxillinBiologySignal transductionCell migrationCellFocal adhesionReceptorCadherin
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Activation of the Met receptor tyrosine kinase through its ligand, hepatocyte growth factor (HGF), promotes an epithelial-mesenchymal transition and cell dispersal. However, little is known about the HGF-dependent signals that regulate these events. HGF stimulation of epithelial cell colonies leads to the enhanced recruitment of the CrkII and CrkL adapter proteins to Met-dependent signaling complexes. Overexpression of Crk adapter proteins in MDCK cells promotes spreading and loss of adherens junctions, events regulated by HGF. We recently demonstrated that the overexpression of CrkII promotes the formation of a multi-molecular complex containing CrkII, Paxillin and GIT-2, an ARF-GAP. MATERIALS AND METHODS: To determine the possible role of ARF1 and ARF6 in Crk- and HGF-dependent cell spreading, dominant negative mutants of ARF1 or ARF6 were microinjected into MDCK cells. RESULTS: We report that MDCK cell lines overexpressing CrkII display reduced ARF6 but not ARF1 activity. While both ARF1 and ARF6 are required for the spreading of MDCK cells stimulated with HGF, ARF1 and ARF6 activity is dispensable for the spreading of cells microinjected with Crk. CONCLUSION: We propose that Crk adapter proteins may act downstream of ARF1 and ARF6 to promote cell spreading or rely on different pathways to enhance cell spreading.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.414

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 head0.028
GPT teacher head0.235
Teacher spread0.207 · 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 designObservational
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

Citations2
Published2003
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicLiver physiology and pathologyFrench-language works237,207