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Record W4321447426 · doi:10.1085/jgp.202213205

Mechanism of external K+ sensitivity of KCNQ1 channels

2023· article· en· W4321447426 on OpenAlexafffund
Astghik Abrahamyan, Jodene Eldstrom, Harutyun Sahakyan, Nare Karagulyan, L. S. Mkrtchyan, Tatev Karapetyan, Ernest Sargsyan, Matthias Kneussel, Karen Nazaryan, Jürgen R. Schwarz, David Fedida, Vitya Vardanyan

Bibliographic record

VenueThe Journal of General Physiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchVolkswagen FoundationSwedish InstituteArmenian National Science and Education FundUniversität HamburgHeart and Stroke Foundation of Canada
KeywordsChemistryBiophysicsConductanceMembrane potentialIon channelPotassium channelBiologyPhysicsBiochemistryReceptor

Abstract

fetched live from OpenAlex

KCNQ1 voltage-gated K+ channels are involved in a wide variety of fundamental physiological processes and exhibit the unique feature of being markedly inhibited by external K+. Despite the potential role of this regulatory mechanism in distinct physiological and pathological processes, its exact underpinnings are not well understood. In this study, using extensive mutagenesis, molecular dynamics simulations, and single-channel recordings, we delineate the molecular mechanism of KCNQ1 modulation by external K+. First, we demonstrate the involvement of the selectivity filter in the external K+ sensitivity of the channel. Then, we show that external K+ binds to the vacant outermost ion coordination site of the selectivity filter inducing a diminution in the unitary conductance of the channel. The larger reduction in the unitary conductance compared to whole-cell currents suggests an additional modulatory effect of external K+ on the channel. Further, we show that the external K+ sensitivity of the heteromeric KCNQ1/KCNE complexes depends on the type of associated KCNE subunits.

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 score0.050
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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