Genetically encoded intrabody sensors report the interaction and trafficking of β-arrestin 1 upon activation of G-protein–coupled receptors
Bibliographic record
Abstract
Agonist stimulation of G-protein-coupled receptors (GPCRs) typically leads to phosphorylation of GPCRs and binding to multifunctional proteins called β-arrestins (βarrs). The GPCR-βarr interaction critically contributes to GPCR desensitization, endocytosis, and downstream signaling, and GPCR-βarr complex formation can be used as a generic readout of GPCR and βarr activation. Although several methods are currently available to monitor GPCR-βarr interactions, additional sensors to visualize them may expand the toolbox and complement existing methods. We have previously described antibody fragments (FABs) that recognize activated βarr1 upon its interaction with the vasopressin V2 receptor C-terminal phosphopeptide (V2Rpp). Here, we demonstrate that these FABs efficiently report the formation of a GPCR-βarr1 complex for a broad set of chimeric GPCRs harboring the V2R C terminus. We adapted these FABs to an intrabody format by converting them to single-chain variable fragments and used them to monitor the localization and trafficking of βarr1 in live cells. We observed that upon agonist simulation of cells expressing chimeric GPCRs, these intrabodies first translocate to the cell surface, followed by trafficking into intracellular vesicles. The translocation pattern of intrabodies mirrored that of βarr1, and the intrabodies co-localized with βarr1 at the cell surface and in intracellular vesicles. Interestingly, we discovered that intrabody sensors can also report βarr1 recruitment and trafficking for several unmodified GPCRs. Our characterization of intrabody sensors for βarr1 recruitment and trafficking expands currently available approaches to visualize GPCR-βarr1 binding, which may help decipher additional aspects of GPCR signaling and regulation.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".