Expression of the Human Glucokinase Gene: Important Roles of the 5′ Flanking and Intron 1 Sequences
Bibliographic record
Abstract
BACKGROUND: Glucokinase plays important tissue-specific roles in human physiology, where it acts as a sensor of blood glucose levels in the pancreas, and a few other cells of the gut and brain, and as the rate-limiting step in glucose metabolism in the liver. Liver-specific expression is driven by one of the two tissue-specific promoters, and has an absolute requirement for insulin. The sequences that mediate regulation by insulin are incompletely understood. METHODOLOGY/PRINCIPAL FINDINGS: To better understand the liver-specific expression of the human glucokinase gene we compared the structures of this gene from diverse mammals. Much of the sequence located between the 5' pancreatic beta-cell-specific and downstream liver-specific promoters of the glucokinase genes is composed of repetitive DNA elements that were inserted in parallel on different mammalian lineages. The transcriptional activity of the liver-specific promoter 5' flanking sequences were tested with and without downstream intronic sequences in two human liver cells lines, HepG2 and L-02. While glucokinase liver-specific 5' flanking sequences support expression in liver cell lines, a sequence located about 2000 bases 3' to the liver-specific mRNA start site represses gene expression. Enhanced reporter gene expression was observed in both cell lines when cells were treated with fetal calf serum, but only in the L-02 cells was expression enhanced by insulin. CONCLUSIONS/SIGNIFICANCE: Our results suggest that the normal liver L-02 cell line may be a better model to understand the regulation of the liver-specific expression of the human glucokinase gene. Our results also suggest that sequences downstream of the liver-specific mRNA start site have important roles in the regulation of liver-specific glucokinase gene expression.
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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".