p53 promotes the expression of gluconeogenesis-related genes and enhances hepatic glucose production
Bibliographic record
Abstract
BACKGROUND: The p53 tumor suppressor protein is a transcription factor that initiates transcriptional programs aimed at inhibiting carcinogenesis. p53 represses metabolic pathways that support tumor development (such as glycolysis and the pentose phosphate pathway (PPP)) and enhances metabolic pathways that are considered counter-tumorigenic such as fatty acid oxidation. FINDINGS: In an attempt to comprehensively define metabolic pathways regulated by p53, we performed two consecutive high-throughput analyses in human liver-derived cells with varying p53 statuses. A gene expression microarray screen followed by constraint-based modeling (CBM) predicting metabolic changes imposed by the transcriptomic changes suggested a role for p53 in enhancing gluconeogenesis (de novo synthesis of glucose). Examining glucogenic gene expression revealed a p53-dependent induction of genes involved in both gluconeogenesis (G6PC, PCK2) and in supplying glucogenic precursors (glycerol kinase (GK), aquaporin 3 (AQP3), aquaporin 9 (AQP9) and glutamic-oxaloacetic transaminase 1 (GOT1)). Accordingly, p53 augmented hepatic glucose production (HGP) in both human liver cells and primary mouse hepatocytes. CONCLUSIONS: These findings portray p53 as a novel regulator of glucose production. By facilitating glucose export, p53 may prevent it from being shunted to pro-cancerous pathways such as glycolysis and the PPP. Thus, our findings suggest a metabolic pathway through which p53 may inhibit tumorigenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".