β-Arrestin-mediated activation of MAPK by inverse agonists reveals distinct active conformations for G protein-coupled receptors
Bibliographic record
Abstract
It is becoming increasingly clear that signaling via G protein-coupled receptors is a diverse phenomenon involving receptor interaction with a variety of signaling partners. Despite this diversity, receptor ligands are commonly classified only according to their ability to modify G protein-dependent signaling. Here we show that beta2AR ligands like ICI118551 and propranolol, which are inverse agonists for Gs-stimulated adenylyl cyclase, induce partial agonist responses for the mitogen-activated protein kinases extracellular signal-regulated kinase (ERK) 1/2 thus behaving as dual efficacy ligands. ERK1/2 activation by dual efficacy ligands was not affected by ADP-ribosylation of Galphai and could be observed in S49-cyc- cells lacking Galphas indicating that, unlike the conventional agonist isoproterenol, these drugs induce ERK1/2 activation in a Gs/i-independent manner. In contrast, this activation was inhibited by a dominant negative mutant of beta-arrestin and was abolished in mouse embryonic fibroblasts lacking beta-arrestin 1 and 2. The role of beta-arrestin was further confirmed by showing that transfection of beta-arrestin 2 in these knockout cells restored ICI118551 promoted ERK1/2 activation. ICI118551 and propranolol also promoted beta-arrestin recruitment to the receptor. Taken together, these observations suggest that beta-arrestin recruitment is not an exclusive property of agonists, and that ligands classically classified as inverse agonists rely exclusively on beta-arrestin for their positive signaling activity. This phenomenon is not unique to beta2-adrenergic ligands because SR121463B, an inverse agonist on the V2 vasopressin receptor-stimulated adenylyl cyclase, recruited beta-arrestin and stimulated ERK1/2. These results point to a multistate model of receptor activation in which ligand-specific conformations are capable of differentially activating distinct signaling partners.
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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) | 0.001 | 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".