Chronic glucokinase activator treatment activates liver Carbohydrate response element binding protein and improves hepatocyte <scp>ATP</scp> homeostasis during substrate challenge
Bibliographic record
Abstract
AIM: To test the hypothesis that glucokinase activators (GKAs) induce hepatic adaptations that alter intra-hepatocyte metabolite homeostasis. METHODS: C57BL/6 mice on a standard rodent diet were treated with a GKA (AZD1656) acutely or chronically. Hepatocytes were isolated from the mice after 4 or 8 weeks of treatment for analysis of cellular metabolites and gene expression in response to substrate challenge. RESULTS: Acute exposure of mice to AZD1656 or a liver-selective GKA (PF-04991532), before a glucose tolerance test, or challenge of mouse hepatocytes with GKAs ex vivo induced various Carbohydrate response element binding protein (ChREBP) target genes, including Carbohydrate response element binding protein beta isoform (ChREBP-β), Gckr and G6pc. Both glucokinase activation and ChREBP target gene induction by PF-04991532 were dependent on the chirality of the molecule, confirming a mechanism linked to glucokinase activation. Hepatocytes from mice treated with AZD1656 for 4 or 8 weeks had lower basal glucose 6-phosphate levels and improved ATP homeostasis during high substrate challenge. They also had raised basal ChREBP-β mRNA and AMPK-α mRNA (Prkaa1, Prkaa2) and progressively attenuated substrate induction of some ChREBP target genes and Prkaa1 and Prkaa2. CONCLUSIONS: Chronic GKA treatment of C57BL/6 mice for 8 weeks activates liver ChREBP and improves the resilience of hepatocytes to compromised ATP homeostasis during high-substrate challenge. These changes are associated with raised mRNA levels of ChREBP-β and both catalytic subunits of AMP-activated protein kinase.
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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) | 0.001 | 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".