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Record W4289262290 · doi:10.1073/pnas.2121225119

Low signaling efficiency from receptor to effector in olfactory transduction: A quantified ligand-triggered GPCR pathway

2022· article· en· W4289262290 on OpenAlexafffund
Rong-Chang Li, Laurie L. Molday, Chih-Chun Lin, Xiaozhi Ren, Alexander Fleischmann, Robert S. Molday, King‐Wai Yau

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2022
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Neurological Disorders and StrokeNational Eye InstituteNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsVisual phototransductionG protein-coupled receptorEffectorSignal transductionReceptorTransduction (biophysics)Cell biologyBiologyOlfactory receptorLigand (biochemistry)RhodopsinChemistryBiochemistryRetinal

Abstract

fetched live from OpenAlex

G protein–coupled receptor (GPCR) signaling is ubiquitous. As an archetype of this signaling motif, rod phototransduction has provided many fundamental, quantitative details, including a dogma that one active GPCR molecule activates a substantial number of downstream G protein/enzyme effector complexes. However, rod phototransduction is light-activated, whereas GPCR pathways are predominantly ligand-activated. Here, we report a detailed study of the ligand-triggered GPCR pathway in mammalian olfactory transduction, finding that an odorant-receptor molecule when (one-time) complexed with its most effective odorants produces on average much less than one downstream effector. Further experiments gave a nominal success probability of tentatively ∼10 −4 (more conservatively, ∼10 −2 to ∼10 −5 ). This picture is potentially more generally representative of GPCR signaling than is rod phototransduction, constituting a paradigm shift.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.148
GPT teacher head0.291
Teacher spread0.143 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

Explore more

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