Regulation of GIP and GLP1 Receptor Cell Surface Expression by N-Glycosylation and Receptor Heteromerization
Bibliographic record
Abstract
In response to a meal, Glucose-dependent Insulinotropic Polypeptide (GIP) and Glucagon-like Peptide-1 (GLP-1) are released from gut endocrine cells into the circulation and interact with their cognate G-protein coupled receptors (GPCRs). Receptor activation results in tissue-selective pleiotropic responses that include augmentation of glucose-induced insulin secretion from pancreatic beta cells. N-glycosylation and receptor oligomerization are co-translational processes that are thought to regulate the exit of functional GPCRs from the ER and their maintenance at the plasma membrane. Despite the importance of these regulatory processes, their impact on functional expression of GIP and GLP-1 receptors has not been well studied. Like many family B GPCRs, both the GIP and GLP-1 receptors possess a large extracellular N-terminus with multiple consensus sites for Asn-linked (N)-glycosylation. Here, we show that each of these Asn residues is glycosylated when either human receptor is expressed in Chinese hamster ovary cells. N-glycosylation enhances cell surface expression and function in parallel but exerts stronger control over the GIP receptor than the GLP-1 receptor. N-glycosylation mainly lengthens receptor half-life by reducing degradation in the endoplasmic reticulum. N-glycosylation is also required for expression of the GIP receptor at the plasma membrane and efficient GIP potentiation of glucose-induced insulin secretion from the INS-1 pancreatic beta cell line. Functional expression of a GIP receptor mutant lacking N-glycosylation is rescued by co-expressed wild type GLP1 receptor, which, together with data obtained using Bioluminescence Resonance Energy Transfer, suggests formation of a GIP-GLP1 receptor heteromer.
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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".