Human glucagon gene promoter sequences regulating tissue-specific versus nutrient-regulated gene expression
Bibliographic record
Abstract
The glucagon-like peptides (GLPs) are synthesized and secreted in a nutrient-dependent manner in rodents; however, the factors regulating human GLP-1 and GLP-2 biosynthesis remain unclear. To understand how nutrients regulate human proglucagon gene expression, we studied the expression of a human proglucagon promoter-growth hormone (GH) transgene in 1.6 human glucagon-GH transgenic mice. Fasting-refeeding significantly decreased and increased the levels of circulating mouse insulin and transgene-derived hGH (P < 0.05 fasting vs. refeeding) and decreased and upregulated, respectively, the levels of endogenous mouse proglucagon RNA in the ileum but not in the jejunum or colon. High-fiber feeding significantly increased the levels of glucose-stimulated circulating hGH and upregulated levels of mouse intestinal proglucagon gene expression in the jejunum, ileum, and colon (P < 0.05, 0 vs. 30% fiber diet). In contrast, neither fasting-refeeding nor a high-fiber diet upregulated the expression of the human proglucagon promoter-hGH transgene. These findings demonstrate that human proglucagon gene regulatory sequences specifying tissue-specific expression in gut endocrine cells are not sufficient for recognition of energy-derived signals regulating murine glucagon gene expression in enteroendocrine cells in vivo.
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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) | 0.002 | 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".