In Vivo Expression of HGF/NK1 and GLP-1 From dsAAV Vectors Enhances Pancreatic β-Cell Proliferation and Improves Pathology in the <i>db/db</i> Mouse Model of Diabetes
Bibliographic record
Abstract
OBJECTIVE: The purpose of the current study was to determine whether double-stranded adeno-associated virus (dsAAV)-mediated in vivo expression of β-cell growth factors, glucagon-like peptide-1 (GLP-1) and the NK1 fragment of hepatocyte growth factor (HGF/NK1) in β-cells, improves pathology in the db/db mouse model of type 2 diabetes. RESEARCH DESIGN AND METHODS; The glucoregulatory actions of GLP-1 and full-length HGF are well characterized. Here, we test the ability of HGF/NK1 to induce proliferation of exogenous islets and MIN6 β-cells. In addition, we target both GLP-1 and HGF/NK1 to endogenous β-cells using dsAAV vectors containing the mouse insulin-II promoter. We compare the abilities of these gene products to induce islet proliferation in vitro and in vivo and characterize their abilities to regulate diabetes after AAV-mediated delivery to endogenous islets of db/db mice. RESULTS: Recombinant HGF/NK1 induces proliferation of isolated islets, and dsAAV-mediated expression of both GLP-1 and HGF/NK1 induces significant β-cell proliferation in vivo. Furthermore, both GLP-1 and HGF/NK1 expressed from dsAAV vectors enhance β-cell mass and insulin secretion in vivo and significantly delay the onset of hyperglycemia in db/db mice. CONCLUSIONS: A single treatment with dsAAV vectors expressing GLP-1 or HGF/NK1 enhances islet growth and significantly improves pathology in a mouse model of type 2 diabetes. This represents the first example of a successful use of HGF/NK1 for diabetes therapy, providing support for direct AAV-mediated in vivo delivery of β-cell growth factors as a novel therapeutic strategy 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 | 0.000 |
| 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 | 0.000 |
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".