MétaCan
Menu
Back to cohort
Record W4395669874 · doi:10.1101/2024.04.25.591203

T2R14 mediated antimicrobial responses through interactions with CFTR

2024· preprint· en· W4395669874 on OpenAlexaff
Tejas M. Gupte, Nisha Singh, Vikram Bhatia, Kavisha Arora, Shayan Amiri, Paul Fernhyhough, Anjaparavanda P. Naren, Shyamala Dakshinamurti, Prashen Chelikani

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsSt. Boniface HospitalUniversity of Manitoba
Fundersnot available
KeywordsAntimicrobialChemistryPharmacologyMicrobiologyBiology

Abstract

fetched live from OpenAlex

ABSTRACT Bitter taste receptors (T2Rs), are a subset of G protein-coupled receptors (GPCRs) that play a key role in responding to microbial presence at epithelial surfaces. In epithelia, the activities of ion channels and transporters, and of T2Rs, mutually affect each other. The normal function of one such anion channel, cystic fibrosis transmembrane conductance regulator (CFTR), is essential for the maintenance of healthy epithelia, not just in the respiratory but in the digestive and reproductive system as well. Based on evidence that T2R14 activity is affected upon mutations in CFTR , we explored the possibility that T2R14 and CFTR directly interact in cell membranes. The biophysical interaction between these proteins was mapped to specific regions of the CFTR, and was dependent on agonist stimulation of T2R14. Further, T2R14 was found to couple to Gαq, in addition to the canonical Gαi, in response to bacterial and fungal quorum sensing molecules. Whether the interaction with CFTR affects T2R14 driven responses to microbial signals is under investigation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicBiochemical Analysis and Sensing TechniquesFrench-language works237,207