Conformation- and activation-based BRET sensors differentially report on GPCR–G protein coupling
Bibliographic record
Abstract
G protein–coupled receptors (GPCRs) regulate cellular signaling processes by coupling to diverse combinations of heterotrimeric G proteins composed of Gα, Gβ, and Gγ subunits. Biosensors based on bioluminescence resonance energy transfer (BRET) have advanced our understanding of GPCR functional selectivity. Some BRET biosensors monitor ligand-induced conformational changes in the receptor or G proteins, whereas others monitor the recruitment of downstream effectors to sites of G protein activation. Here, we compared the ability of conformation-and activation-based BRET biosensors to assess the coupling of various class A and B GPCRs to specific Gα proteins in cultured cells. These GPCRs included serotonin 5-HT 2A and 5-HT 7 receptors, the GLP-1 receptor (GLP-1R), and the M 3 muscarinic receptor. We observed different signaling profiles between the two types of sensors, highlighting how data interpretation could be affected by the nature of the biosensor. We also found that the identity of the Gβγ subunits used in the assay could differentially influence the selectivity of a receptor toward Gα subtypes, emphasizing the importance of the receptor-Gβγ pairing in determining Gα coupling specificity. Last, the addition of epitope tags to the receptor could affect stoichiometry and coupling selectivity and yield artifactual findings. These results highlight the need for careful sensor selection and experimental design when probing GPCR–G protein coupling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".