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Record W2037552851 · doi:10.1074/jbc.m307772200

Cross-talk and Co-trafficking between ρ1/GABA Receptors and ATP-gated Channels

2004· article· en· W2037552851 on OpenAlexaff
Éric Boué‐Grabot, M.B. Emerit, Estelle Toulmé, Philippe Séguéla, Maurice Garret

Bibliographic record

VenueJournal of Biological Chemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsGABAA receptorReceptorChemistryCell biologyNeuroscienceBiophysicsBiologyBiochemistry

Abstract

fetched live from OpenAlex

Gamma-aminobutyric-acid (GABA) and ATP ionotropic receptors represent two structurally and functionally different classes of neurotransmitter-gated channels involved in fast synaptic transmission. We demonstrate here that, when the inhibitory rho1/GABA and the excitatory P2X2 receptor channels are co-expressed in Xenopus oocytes, activation of one channel reduces the currents mediated by the other one. This reciprocal inhibitory cross-talk is a receptor-mediated phenomenon independent of agonist cross-modulation, membrane potential, direction of ionic flux, or channel densities. Functional interaction is disrupted when the cytoplasmic C-terminal domain of P2X2 is deleted or in competition experiments with minigenes coding for the C-terminal domain of P2X2 or the main intracellular loop of rho1 subunits. We also show a physical interaction between P2X2 and rho1 receptors expressed in oocytes and the co-clustering of these receptors in transfected hippocampal neurons. Co-expression with P2X2 induces retargeting and recruitment of mainly intracellular rho1/GABA receptors to surface clusters. Therefore, molecular and functional cross-talk between inhibitory and excitatory ligand-gated channels may regulate synaptic strength both by activity-dependent current occlusion and synaptic receptors co-trafficking.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.508

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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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

Citations61
Published2004
Admission routes1
Has abstractyes

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