Pancreatic β-cell overexpression of the glucagon receptor gene results in enhanced β-cell function and mass
Bibliographic record
Abstract
In addition to its primary role in regulating glucose production from the liver, glucagon has many other actions, reflected by the wide tissue distribution of the glucagon receptor (Gcgr). To investigate the role of glucagon in the regulation of insulin secretion and whole body glucose homeostasis in vivo, we generated mice overexpressing the Gcgr specifically on pancreatic beta-cells (RIP-Gcgr). In vivo and in vitro insulin secretion in response to glucagon and glucose was increased 1.7- to 3.9-fold in RIP-Gcgr mice compared with controls. Consistent with the observed increase in insulin release in response to glucagon and glucose, the glucose excursion resulting from both a glucagon challenge and intraperitoneal glucose tolerance test (IPGTT) was significantly reduced in RIP-Gcgr mice compared with controls. However, RIP-Gcgr mice display similar glucose responses to an insulin challenge. beta-Cell mass and pancreatic insulin content were also increased (20 and 50%, respectively) in RIP-Gcgr mice compared with controls. When fed a high-fat diet (HFD), both control and RIP-Gcgr mice developed similar degrees of obesity and insulin resistance. However, the severity of both fasting hyperglycemia and impaired glucose tolerance (IGT) were reduced in RIP-Gcgr mice compared with controls. Furthermore, the insulin response of RIP-Gcgr mice to an IPGTT was twice that of controls when fed the HFD. These data indicate that increased pancreatic beta-cell expression of the Gcgr increased insulin secretion, pancreatic insulin content, beta-cell mass, and, when mice were fed a HFD, partially protected against hyperglycemia and IGT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".