Engineered mini-G proteins block the internalization of cognate GPCRs and disrupt downstream intracellular signaling
Bibliographic record
Abstract
Mini-G proteins are engineered, thermostable variants of Gα subunits designed to stabilize G protein–coupled receptors (GPCRs) in their active conformations. Because of their small size and ease of use, they are popular tools for assessing GPCR behaviors in cells, both as reporters of receptor coupling to Gα subtypes and for cellular assays to quantify compartmentalized signaling at various subcellular locations. Here, we report that overexpression of mini-G proteins with their cognate GPCRs disrupted GPCR endocytic trafficking and associated intracellular signaling. In cells expressing the Gα s -coupled GPCR glucagon-like peptide 1 receptor (GLP-1R), coexpression of mini-G s , a mini-G protein derived from Gα s , blocked β-arrestin 2 recruitment and receptor internalization and disrupted endosomal GLP-1R signaling. These effects did not involve changes in receptor phosphorylation or lipid nanodomain segregation. Moreover, we found that mini-G proteins derived from Gα i and Gα q also inhibited the internalization of GPCRs that couple to them. Finally, we developed an alternative intracellular signaling assay for GLP-1R using a nanobody specific for active Gα s :GPCR complexes (Nb37) that did not affect GLP-1R internalization. Our results have important implications for designing methods to assess intracellular GPCR signaling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".