Gαs is dispensable for β-arrestin coupling but dictates GRK selectivity and is predominant for gene expression regulation by β2-adrenergic receptor
Bibliographic record
Abstract
β-arrestins play a key role in G protein–coupled receptor (GPCR) internalization, trafficking, and signaling. Whether β-arrestins act independently of G protein–mediated signaling has not been fully elucidated. Studies using genome-editing approaches revealed that whereas G proteins are essential for mitogen-activated protein kinase activation by GPCRs., β-arrestins play a more prominent role in signal compartmentalization. However, in the absence of G proteins, GPCRs may not activate β-arrestins, thereby limiting the ability to distinguish G protein from β-arrestin-mediated signaling events. We used β2-adrenergic receptor (β2AR) and its β2AR-C tail mutant expressed in human embryonic kidney 293 cells wildtype or CRISPR–Cas9 gene edited for Gα s , β-arrestin1/2, or GPCR kinases 2/3/5/6 in combination with arrestin conformational sensors to elucidate the interplay between Gα s and β-arrestins in controlling gene expression. We found that Gα s is not required for β2AR and β-arrestin conformational changes, β-arrestin recruitment, and receptor internalization, but that Gα s dictates the GPCR kinase isoforms involved in β-arrestin recruitment. By RNA-Seq analysis, we found that protein kinase A and mitogen-activated protein kinase gene signatures were activated by stimulation of β2AR in wildtype and β-arrestin1/2-KO cells but absent in Gα s -KO cells. These results were validated by re-expressing Gα s in the corresponding KO cells and silencing β-arrestins in wildtype cells. These findings were extended to cellular systems expressing endogenous levels of β2AR. Overall, our results support that Gs is essential for β2AR-promoted protein kinase A and mitogen-activated protein kinase gene expression signatures, whereas β-arrestins initiate signaling events modulating Gα s -driven nuclear transcriptional activity.
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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) | 0.000 | 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) | 0.003 | 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".