Pancreatic islet-specific overexpression of Reg3β protein induced the expression of pro-islet genes and protected the mice against streptozotocin-induced diabetes mellitus
Bibliographic record
Abstract
Reg family proteins have been implicated in islet β-cell proliferation, survival, and regeneration. The expression of Reg3β (pancreatitis-associated protein) is highly induced in experimental diabetes and acute pancreatitis, but its precise role has not been established. Through knockout studies, this protein was shown to be mitogenic, antiapoptotic, and anti-inflammatory in the liver and pancreatic acinars. To test whether it can promote islet cell growth or survival against experimental damage, we developed β-cell-specific overexpression using rat insulin I promoter, evaluated the changes in normal islet function, gene expression profile, and the response to streptozotocin-induced diabetes. Significant and specific overexpression of Reg3β was achieved in the pancreatic islets of RIP-I/Reg3β mice, which exhibited normal islet histology, β-cell mass, and in vivo and in vitro insulin secretion in response to high glucose yet were slightly hyperglycemic and low in islet GLUT2 level. Upon streptozotocin treatment, in contrast to wild-type littermates that became hyperglycemic in 3 days and lost 15% of their weight, RIP-I/Reg3β mice were significantly protected from hyperglycemia and weight loss. To identify specific targets affected by Reg3β overexpression, a whole genome DNA microarray on islet RNA isolated from the transgenic mice revealed more than 45 genes significantly either up- or downregulated. Among them, islet-protective osteopontin/SPP1 and acute responsive nuclear protein p8/NUPR1 were significantly induced, a result further confirmed by real-time PCR, Western blots, and immunohistochemistry. Our results suggest that Reg3β is unlikely an islet growth factor but a putative protector that prevents streptozotocin-induced damage by inducing the expression of specific genes.
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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 | 0.000 | 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".