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Na+/Cl- Dipole Couples Agonist Binding to Kainate Receptor Activation

2007· article· en· W2011275152 on OpenAlexafffund
Adrian Y. C. Wong, David M. MacLean, Derek Bowie

Bibliographic record

VenueJournal of Neuroscience · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsAgonistKainate receptorReceptorChemistryNeuroscienceBiologyBiochemistryAMPA receptorGlutamate receptor

Abstract

fetched live from OpenAlex

Kainate-selective ionotropic glutamate receptors (GluRs) require external Na+ and Cl- as well as the neurotransmitter L-glutamate for activation. Although, external anions and cations apparently coactivate kainate receptors (KARs) in an identical manner, it has yet to be established how ions of opposite charge achieve this. An additional complication is that KARs are subject to other forms of cation modulation via extracellular acidification (i.e., protons) and divalent ions. Consequently, other cation species may compete with Na+ to regulate the time KARs remain in the open state. Here we designed experiments to unravel how external ions regulate GluR6 KARs. We show that GluR6 kinetics are unaffected by alterations in physiological pH but that divalent and alkali metal ions compete to determine the time course of KAR channel activity. Additionally, Na+ and Cl- ions coactivate GluR6 receptors by establishing a dipole, accounting for their common effect on KARs. Using charged amino acids as tethered ions, we further demonstrate that the docking order is fixed with cations binding first, followed by anions. Together, our findings identify the dipole as a novel gating feature that couples neurotransmitter binding to KAR activation.

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 score0.117
Threshold uncertainty score0.217

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 head0.023
GPT teacher head0.283
Teacher spread0.260 · 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

Citations33
Published2007
Admission routes2
Has abstractyes

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