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Record W2314638811 · doi:10.1242/jcs.152173

p75NTR-dependent Rac1 activation requires receptor cleavage and activation of an NRAGE and NEDD9 signaling cascade

2014· article· en· W2314638811 on OpenAlexafffund
Michele Zeinieh, Amir H. Salehi, V Rajkumar, Philip Barker

Bibliographic record

VenueJournal of Cell Science · 2014
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health Research
KeywordsBiologyRAC1Cell biologyCleavage (geology)CascadeReceptorSignal transductionCancer researchNeuroscienceGeneticsEngineering

Abstract

fetched live from OpenAlex

The p75 neurotrophin receptor (p75NTR, also known as tumor necrosis factor receptor superfamily member 16) is implicated in diverse cellular events, but fundamental aspects of its signaling mechanisms remain unclear. To address this, we have established a novel bioassay to characterize signaling cascades activated by p75NTR. We show that in COS7 cells, p75NTR expression causes a large increase in cell surface area that relies on the activation of Rac1, and we demonstrate that the p75NTR-dependent COS7 phenotype is dependent on ADAM17- and c-secretase-dependent cleavage of p75NTR and generation of the p75NTR intracellular domain (p75NTRICD). We show that the p75NTR adaptor protein NRAGE (also known as MAGED1) acts downstream of the p75NTRICD in this cascade and, through a yeast two-hybrid screen, identify NEDD9, a Cas family adaptor protein, as a novel NRAGE-binding partner that mediates p75NTR-dependent Rac1 activation and cell spreading. Our results demonstrate a crucial role for p75NTR cleavage in small GTPase activation and define a novel Rac1 activation pathway involving the p75NTRICD, NRAGE andNEDD9.

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 score0.001
Threshold uncertainty score0.004

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.275
Teacher spread0.248 · 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

Citations14
Published2014
Admission routes2
Has abstractyes

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