Dysregulated transmethylation leading to hepatocellular carcinoma compromises redox homeostasis and glucose formation
Bibliographic record
Abstract
Objective The loss of liver glycine N-methyltransferase (GNMT) promotes liver steatosis and the transition to hepatocellular carcinoma (HCC). Previous work showed endogenous glucose production is reduced in GNMT-null mice with gluconeogenic precursors being used in alternative biosynthetic pathways that utilize methyl donors and are linked to tumorigenesis. This metabolic programming occurs before the appearance of HCC in GNMT-null mice. The metabolic physiology that sustains liver tumor formation in GNMT-null mice is unknown. The studies presented here tested the hypothesis that nutrient flux pivots from glucose production to pathways that incorporate and metabolize methyl groups in GNMT-null mice with HCC. Methods 2 H/ 13 C metabolic flux analysis was performed in conscious, unrestrained mice lacking GNMT to quantify glucose formation and associated nutrient fluxes. Molecular analyses of livers from mice lacking GNMT including metabolomic, immunoblotting, and immunochemistry were completed to fully interpret the nutrient fluxes. Results GNMT knockout (KO) mice showed lower blood glucose that was accompanied by a reduction in liver glycogenolysis and gluconeogenesis. NAD + was lower and the NAD(P)H-to-NAD(P) + ratio was higher in livers of KO mice. Indices of NAD + synthesis and catabolism, pentose phosphate pathway flux, and glutathione synthesis were dysregulated in KO mice. Conclusion Glucose precursor flux away from glucose formation towards pathways that regulate redox status increase in the liver. Moreover, synthesis and scavenging of NAD + are both impaired resulting in reduced concentrations. This metabolic program blunts an increase in methyl donor availability, however, biosynthetic pathways underlying HCC are activated.
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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".