Discovery of Dual-Action Membrane-Anchored Modulators of Incretin Receptors
Bibliographic record
Abstract
BACKGROUND: The glucose-dependent insulinotropic polypeptide (GIP) and the glucagon-like peptide-1 (GLP-1) receptors are considered complementary therapeutic targets for type 2 diabetes. Using recombinant membrane-tethered ligand (MTL) technology, the present study focused on defining optimized modulators of these receptors, as well as exploring how local anchoring influences soluble peptide function. METHODOLOGY/PRINCIPAL FINDINGS: Serial substitution of residue 7 in membrane-tethered GIP (tGIP) led to a wide range of activities at the GIP receptor, with [G(7)]tGIP showing enhanced efficacy compared to the wild type construct. In contrast, introduction of G(7) into the related ligands, tGLP-1 and tethered exendin-4 (tEXE4), did not affect signaling at the cognate GLP-1 receptor. Both soluble and tethered GIP and GLP-1 were selective activators of their respective receptors. Although soluble EXE4 is highly selective for the GLP-1 receptor, unexpectedly, tethered EXE4 was found to be a potent activator of both the GLP-1 and GIP receptors. Diverging from the pharmacological properties of soluble and tethered GIP, the newly identified GIP-R agonists, (i.e. [G(7)]tGIP and tEXE4) failed to trigger cognate receptor endocytosis. In an attempt to recapitulate the dual agonism observed with tEXE4, we conjugated soluble EXE4 to a lipid moiety. Not only did this soluble peptide activate both the GLP-1 and GIP receptors but, when added to receptor expressing cells, the activity persists despite serial washes. CONCLUSIONS: These findings suggest that conversion of a recombinant MTL to a soluble membrane anchored equivalent offers a means to prolong ligand function, as well as to design agonists that can simultaneously act on more than one therapeutic target.
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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".