Biased signaling regulates the pleiotropic effects of the urotensin II receptor to modulate its cellular behaviors
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Bibliographic record
Abstract
Biased agonism by G-protein-coupled receptor ligands has opened up strategies for targeted physiological or therapeutic actions. We hypothesized that urotensin II (UII)-derived peptides displayed unexpected physiological effects because of such biased signaling on the UII human urotensin (hUT) receptor. We determined the coupling to G proteins and β-arrestins of the UII-activated hUT receptor expressed in HEK293 using bioluminescence resonance energy transfer (BRET) biosensors, as well as the production of IP1-3 and cAMP using homogenous time-resolved Forster resonance energy transfer (FRET) (HTRF)-based assays. The activated receptor coupled to Gi1, GoA, Gq, and G13, excluding Gs, and recruited β-arrestins 1 and 2. Integration of these pathways led to a 2-phase kinetic phosphorylation of ERK1/2 kinases. The tested peptides induced three different profiles: UII, urotensin-related peptide (URP), and UII4-11 displayed the full profile; [Orn(8)]UII and [Orn(5)]URP activated G proteins, although with pEC50s 5-10× higher, and did not or barely recruited β-arrestin; urantide also failed to recruit β-arrestin but displayed a reversed rank order for Gi and Gq vs. Go pEC50s (-8.79±0.20, -8.43±0.21, and -7.86±0.36, respectively, for urantide, -7.87±0.10, -7.23±0.27, and -8.55±0.19, respectively, for [Orn(5)]URP) and was a partial agonist of all G-protein pathways. Interestingly, the peptides differently modulated cell survival but similarly induced cell migration and adhesion. Thus, we demonstrate biased signaling between β-arrestin and G proteins, and between G-protein subtypes, which dictates the receptor's cellular responses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it