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Record W3132371458 · doi:10.1111/febs.15768

Critically evaluating sweet taste receptor expression and signaling through a molecular pharmacology lens

2021· review· en· W3132371458 on OpenAlexaff
Nicola J. Smith, Jennifer N. Grant, Justin I. Moon, Sean S. So, Angela M. Finch

Bibliographic record

VenueFEBS Journal · 2021
Typereview
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsKensington Health
FundersNational Heart Foundation of Australia
KeywordsReceptorBiologySignal transductionTasteTaste receptorComputational biologyGeneticsCell biologyNeuroscience

Abstract

fetched live from OpenAlex

The class C G protein-coupled sweet taste receptor (STR) is responsible for the perception of sweet-tasting molecules. Considered an obligate heterodimer, it consists of taste 1 receptor 2 and taste 1 receptor 3 subunits. Interest in the STR has steadily grown, especially since its discovery in extraoral tissues hints at a metabolic role for the receptor. It is now known that many pharmacologically exploitable binding sites exist across the extracellular and transmembrane regions of both subunits of the STR, indicative of its potential amenability to pharmacotherapeutic modulation. In this review, we briefly describe the structural characteristics and functional relevance of the STR. Then, from a molecular pharmacology perspective, we dissect the research surrounding the regulation of STR surface expression and signal transduction, in both oral and extraoral tissues, and discuss the potential for the exploitation of biased agonists for the STR. We find that despite 20 years of research into the STR, the target remains frustratingly enigmatic. Not only are the mechanisms controlling and regulating the surface expression of the STR unclear, but also research into the full repertoire of signaling partners of the STR is at present inconclusive. Critically, the influence of receptor polymorphisms (including those associated with sugar consumption) on the molecular pharmacology of the receptor remains hitherto unexplored. Finally, we provide recommendations on the reporting of reference sequence identification numbers to avoid incorrect attribution of wild-type to these biologically significant polymorphisms, which we argue may have led to some of the inconsistencies in the field.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.091
GPT teacher head0.424
Teacher spread0.333 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations25
Published2021
Admission routes1
Has abstractyes

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