G Protein-Biased Agonists for Intracellular Angiotensin Receptors Promote Collagen Secretion in Myofibroblasts
Bibliographic record
Abstract
Photoactivatable ligands remain valuable tools to study the spatiotemporal aspects of cellular signaling. However, the synthesis, handling, and biological validation of such compounds remain challenging, especially when dealing with peptides. We report an optimized synthetic strategy, where laborious preparation of dimethoxy-nitrobenzyl-tyrosine building blocks was replaced by direct functionalization of amino acid side chains while peptides remained coupled to resin, reducing both preparation time and cost. Our caged peptides were designed to investigate cellular responses mediated by intracellular angiotensin II receptors ( i ATR) upon interaction with known biased and unbiased ligands. The pathophysiological roles of i ATRs remain poorly understood, and we sought to develop ligands to explore this. Initial validation showed that our caged ligands undergo rapid photolysis and produced functionally active peptides upon UV exposure. We also show, for the first time, that different biased ligands (β-arrestin- vs G protein-biased analogues) evoked distinct responses when uncaged in adult rat myofibroblasts. Intracellularly targeted versions of Ang II (unbiased) or G protein-biased analogues (TRV055, TRV056) were more effective than β-arrestin-biased Ang II analogues (SI, TRV026, and TRV27) in inducing collagen secretion, suggesting a divergent role in regulating the fibrotic response.
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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".