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Record W4285987564 · doi:10.1126/sciadv.abo3625

Electro-mechanical coupling of KCNQ channels is a target of epilepsy-associated mutations and retigabine

2022· article· en· W4285987564 on OpenAlexaff
Nien-Du Yang, Richard Kanyo, Lu Zhao, Jingru Li, Po Wei Kang, Alex Dou, Kelli McFarland White, Jingyi Shi, Jeanne M. Nerbonne, Harley T. Kurata, Jianmin Cui

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsUniversity of Alberta
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood Institute
KeywordsGatingCoupling (piping)EpilepsyMechanism (biology)ChemistryMutationPotassium channelBiophysicsNeuroscienceBiologyBiochemistryMaterials sciencePhysicsGene

Abstract

fetched live from OpenAlex

KCNQ2 and KCNQ3 form the M-channels that are important in regulating neuronal excitability. Inherited mutations that alter voltage-dependent gating of M-channels are associated with neonatal epilepsy. In the homolog KCNQ1 channel, two steps of voltage sensor activation lead to two functionally distinct open states, the intermediate-open (IO) and activated-open (AO), which define the gating, physiological, and pharmacological properties of KCNQ1. However, whether the M-channel shares the same mechanism is unclear. Here, we show that KCNQ2 and KCNQ3 feature only a single conductive AO state but with a conserved mechanism for the electro-mechanical (E-M) coupling between voltage sensor activation and pore opening. We identified some epilepsy-linked mutations in KCNQ2 and KCNQ3 that disrupt E-M coupling. The antiepileptic drug retigabine rescued KCNQ3 currents that were abolished by a mutation disrupting E-M coupling, suggesting that modulating the E-M coupling in KCNQ channels presents a potential strategy for antiepileptic therapy.

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 score0.009
Threshold uncertainty score0.201

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 head0.009
GPT teacher head0.263
Teacher spread0.254 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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