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Record W2134908784 · doi:10.1242/jcs.137679

Interaction of 4.1G and cGMP-gated channels in rod photoreceptor outer segments

2013· article· en· W2134908784 on OpenAlexafffund
Christiana L. Cheng, Robert S. Molday

Bibliographic record

VenueJournal of Cell Science · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsUniversity of British Columbia
FundersNational Eye InstituteCanada Research Chairs
KeywordsVisual phototransductionCyclic nucleotide-gated ion channelBiologyImmunoprecipitationCell biologyIon channelBiophysicsMembrane proteinBiochemistryMembraneNucleotideReceptorRetinalCyclic nucleotideGene

Abstract

fetched live from OpenAlex

In photoreceptors, the assembly of signaling molecules into macromolecular complexes is important for phototransduction and maintaining the structural integrity of rod outer segments (ROSs). However, the molecular composition and formation of these complexes are poorly understood. Using immunoprecipitation and mass spectrometry, 4.1G was identified as a new interacting partner for the cyclic-nucleotide gated (CNG) channels in ROSs. 4.1G is a widely expressed multifunctional protein that plays a role in the assembly and stability of membrane protein complexes. Multiple splice variants of 4.1G were cloned from bovine retina. A smaller splice variant of 4.1G selectively interacted with CNG channels not associated with peripherin-2-CNG channel complex. A combination of truncation studies and domain-binding assays demonstrated that CNG channels selectively interacted with 4.1G through their FERM and CTD domains. Using immunofluorescence, labeling of 4.1G was seen to be punctate and partially colocalized with CNG channels in the ROS. Our studies indicate that 4.1G interacts with a subset of CNG channels in the ROS and implicate this protein-protein interaction in organizing the spatial arrangement of CNG channels in the plasma membrane of outer segments.

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 score0.006
Threshold uncertainty score0.145

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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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

Citations15
Published2013
Admission routes2
Has abstractyes

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