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Record W2340621883 · doi:10.1080/19420889.2016.1175696

Similar requirement for clathrin in EGF- and HGF- stimulated Akt phosphorylation

2016· article· en· W2340621883 on OpenAlexaff
Stefanie Lucarelli, Rohan Pandey, Gurjeet Judge, Costin N. Antonescu

Bibliographic record

VenueCommunicative & Integrative Biology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsSt. Michael's HospitalToronto Metropolitan University
Fundersnot available
KeywordsClathrinProtein kinase BPhosphorylationCell biologyEndocytosisReceptor tyrosine kinaseEpidermal growth factorSignal transductionEpidermal growth factor receptorChemistryTyrosine phosphorylationCancer researchEndosomeBiologyReceptorIntracellularBiochemistry

Abstract

fetched live from OpenAlex

Receptor tyrosine kinases, such as the epidermal growth factor (EGF) receptor (EGFR) and Met lead to activation of intracellular signals including Akt, a critical regulator of cell survival, metabolism and proliferation. Upon binding their respective ligands, each of these receptors is recruited into clathrin coated pits (CCPs) eventually leading to endocytosis. We have recently shown that phosphorylation of Gab1 and Akt following EGFR activation requires clathrin, but does not require receptor endocytosis. We examined whether clathrin regulates Akt signaling downstream of Met, as it does for EGFR signaling. Stimulation with the Met ligand Hepatocyte Growth Factor (HGF) leads to enrichment of phosphorylated Gab1 (pGab1) within CCPs in ARPE-19 cells. Perturbation of clathrin using the inhibitor pitstop2 decreases HGF-stimulated Akt phosphorylation. These results indicate that clathrin may regulate Met signaling leading to Akt phosphorylation similarly as it does for EGFR signaling.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.304
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2016
Admission routes1
Has abstractyes

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