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Record W1988642390 · doi:10.1242/jcs.160374

Dimerization drives EGF receptor endocytosis through two sets of compatible endocytic codes

2015· article· en· W1988642390 on OpenAlexafffund
Qian Wang, Xinmei Chen, Zhixiang Wang

Bibliographic record

VenueJournal of Cell Science · 2015
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsEndocytic cycleEndocytosisBiologyCell biologyERBB3InternalizationReceptor-mediated endocytosisReceptorEpidermal growth factor receptorBiochemistry

Abstract

fetched live from OpenAlex

We have shown previously that epidermal growth factor (EGF) receptor (EGFR) endocytosis is controlled by EGFR dimerization. However, it is not clear how the dimerization drives receptor internalization. We propose that EGFR endocytosis is driven by dimerization, bringing two sets of endocytic codes, one contained in each receptor monomer, in close proximity. Here, we tested this hypothesis by generating specific homo- or hetero-dimers of various receptors and their mutants. We show that ErbB2 and ErbB3 homodimers are endocytosis deficient owing to the lack of endocytic codes. Interestingly, EGFR-ErbB2 or EGFR-ErbB3 heterodimers are also endocytosis deficient. Moreover, the heterodimer of EGFR and the endocytosis-deficient mutant EGFRΔ1005-1017 is also impaired in endocytosis. These results indicate that two sets of endocytic codes are required for receptor endocytosis. We found that an EGFR-PDGFRβ heterodimer is endocytosis deficient, although both EGFR and PDGFRβ homodimers are endocytosis-competent, indicating that two compatible sets of endocytic codes are required. Finally, we found that to mediate the endocytosis of the receptor dimer, the two sets of compatible endocytic codes, one contained in each receptor molecule, have to be spatially coordinated.

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 score0.014
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.064
GPT teacher head0.373
Teacher spread0.309 · 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

Citations24
Published2015
Admission routes2
Has abstractyes

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