Biased signaling regulates the pleiotropic effects of the urotensin II receptor to modulate its cellular behaviors
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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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 | 0.000 |
| 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".