Engineered Enteroendocrine Cells Secrete Insulin in Response to Glucose and Reverse Hyperglycemia in Diabetic Mice
Bibliographic record
Abstract
Type 1 diabetes is a metabolic disorder caused by loss of insulin-producing pancreatic beta-cells. Expression of insulin in non-beta-cells to create beta-cell surrogates has been tried to treat type 1 diabetes. Enteroendocrine K cells have characteristics similar to pancreatic beta-cells, such as a glucose-sensing system and insulin-processing proteases. In this study, we genetically engineered an enteroendocrine cell line (STC-1) to express insulin under the control of the glucose-dependent insulinotropic polypeptide promoter. We screened clones and chose one, Gi-INS-7, based on its high production of insulin. Gi-INS-7 cells expressed glucose transporter 2 (GLUT2) and glucokinase (GK) and secreted insulin in response to elevated glucose levels in vitro. To determine whether Gi-INS-7 cells can control blood glucose levels in diabetic mice, we transplanted these cells under the kidney capsule of streptozotocin (STZ)-induced diabetic mice and found that blood glucose levels became normal within 2 weeks of transplantation. In addition, glucose tolerance tests in mice that became normoglycemic after transplantation with Gi-INS-7 cells showed that exogenous glucose was cleared appropriately. These results suggest that engineered K cells may be promising surrogate beta-cells for possible therapeutic use for the treatment of type 1 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".