A fusion protein derived from plants holds promising potential as a new oral therapy for type 2 diabetes
Bibliographic record
Abstract
The incretin hormone glucagon-like peptide-1 (GLP-1) is recognized as a promising candidate for the treatment of type 2 diabetes (T2D), with one of its mimetics, exenatide (synthetic exendin-4) having already been licensed for clinical use. We seek to further improve the therapeutic efficacy of exendin-4 (Ex-4) using innovative fusion protein technology. Here, we report the production in plants a fusion protein containing Ex-4 coupled with human transferrin (Ex-4-Tf) and its characterization. We demonstrated that plant-made Ex-4-Tf retained the activity of both proteins. In particular, the fusion protein stimulated insulin release from pancreatic β-cells, promoted β-cell proliferation, stimulated differentiation of pancreatic precursor cells into insulin-producing cells, retained the ability to internalize into human intestinal cells and resisted stomach acid and proteolytic enzymes. Importantly, oral administration of partially purified Ex-4-Tf significantly improved glucose tolerance, whereas commercial Ex-4 administered by the same oral route failed to show any significant improvement in glucose tolerance in mice. Furthermore, intraperitoneal (IP) injection of Ex-4-Tf showed a beneficial effect in mice similar to IP-injected Ex-4. We also showed that plants provide a robust system for the expression of Ex-4-Tf, producing up to 37 μg prEx-4-Tf/g fresh leaf weight in transgenic tobacco and 137 μg prEx-4-Tf/g freshweight in transiently transformed leaves of N. benthamiana. These results indicate that Ex-4-Tf holds substantial promise as a new oral therapy for type 2 diabetes. The production of prEx-4-Tf in plants may offer a convenient and cost-effective method to deliver the antidiabetic medicine in partially processed plant food products.
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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 | 0.000 |
| 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".