Engineered glucagon-like peptide-1-producing hepatocytes lower plasma glucose levels in mice
Bibliographic record
Abstract
Glucagon-like peptide (GLP)-1 is an incretin hormone with well-characterized antidiabetic properties, including glucose-dependent stimulation of insulin secretion and enhancement of beta-cell mass. GLP-1 agonists have recently been developed and are now in clinical use for the treatment of type 2 diabetes. Rapid degradation of GLP-1 by enzymes including dipeptidyl-peptidase (DPP)-IV and neutral endopeptidase (NEP) 24.11, along with renal clearance, contribute to a short biological half-life, necessitating frequent injections to maintain therapeutic efficacy. Gene therapy may represent a promising alternative approach for achieving long-term increases in endogenous release of GLP-1. We have developed a novel strategy for glucose-regulated production of GLP-1 in hepatocytes by expressing a DPP-IV-resistant GLP-1 peptide in hepatocytes under control of the liver-type pyruvate kinase promoter. Adenoviral delivery of this construct to hepatocytes in vitro resulted in production and secretion of bioactive GLP-1 as measured by a luciferase-based bioassay developed to detect the NH2-terminally modified GLP-1 peptide engineered for this study. Transplantation of encapsulated hepatocytes into CD-1 mice resulted in an increase in plasma GLP-1 levels that was accompanied by a significant reduction in fasting plasma glucose levels. The results from this study demonstrate that a gene therapy approach designed to induce GLP-1 production in hepatocytes may represent a novel strategy for long-term secretion of bioactive GLP-1 for the treatment of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".