β-Arrestin Recruitment and Biased Agonism at Free Fatty Acid Receptor 1
Bibliographic record
Abstract
FFAR1/GPR40 is a seven-transmembrane domain receptor (7TMR) expressed in pancreatic β cells and activated by FFAs. Pharmacological activation of GPR40 is a strategy under consideration to increase insulin secretion in type 2 diabetes. GPR40 is known to signal predominantly via the heterotrimeric G proteins Gq/11. However, 7TMRs can also activate functionally distinct G protein-independent signaling via β-arrestins. Further, G protein- and β-arrestin-based signaling can be differentially modulated by different ligands, thus eliciting ligand-specific responses ("biased agonism"). Whether GPR40 engages β-arrestin-dependent mechanisms and is subject to biased agonism is unknown. Using bioluminescence resonance energy transfer-based biosensors for real-time monitoring of cell signaling in living cells, we detected a ligand-induced GPR40-β-arrestin interaction, with the synthetic GPR40 agonist TAK-875 being more effective than palmitate or oleate in recruiting β-arrestins 1 and 2. Conversely, TAK-875 acted as a partial agonist of Gq/11-dependent GPR40 signaling relative to both FFAs. Pharmacological blockade of Gq activity decreased FFA-induced insulin secretion. In contrast, knockdown or genetic ablation of β-arrestin 2 in an insulin-secreting cell line and mouse pancreatic islets, respectively, uniquely attenuated the insulinotropic activity of TAK-875, thus providing functional validation of the biosensor data. Collectively, these data reveal that in addition to coupling to Gq/11, GPR40 is functionally linked to a β-arrestin 2-mediated insulinotropic signaling axis. These observations expose previously unrecognized complexity for GPR40 signal transduction and may guide the development of biased agonists showing improved clinical profile in type 2 diabetes.
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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".