PREB regulates transcription of pancreatic glucokinase in response to glucose and cAMP
Bibliographic record
Abstract
Prolactin regulatory element binding (PREB) is a transcription factor that regulates prolactin promoter activity in rat anterior pituitary. The PREB protein is not only expressed in the anterior pituitary but also in the pancreas. We have recently reported that in pancreatic beta-cells, PREB regulates the transcription of the insulin gene in response to glucose stimulation. In the current study, we have examined the role of PREB in regulating glucokinase (GK) in pancreatic beta-cells. To analyse the effects of PREB on GK gene transcription, we employed a reporter gene assay. In the cells expressing or with knocked down PREB, GK expression was determined. GK expression was regulated by glucose and cAMP, and both glucose and cAMP stimulated the expression of PREB in a dose-dependent manner. Conversely, overexpression of PREB using a PREB-expressing adenovirus increased the expression of the GK protein. GK enzymatic activity was also significantly increased in the cells that stably expressed PREB. In addition, PREB induced GK promoter activity. Chromatin immunoprecipitation (ChIP) analyses showed that PREB mediated its transcriptional effect by binding to the PREB-responsive cis-element of the GK promoter. Finally, we used siRNA to inhibit PREB expression in cells and demonstrated that the knockdown of PREB attenuated the effects of glucose and cAMP on GK expression. Our data show that in pancreatic -cells, PREB regulates the transcription of the GK gene in response to glucose and cAMP.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".