Glycine N-methyltransferase deletion in mice diverts carbon flux from gluconeogenesis to pathways that utilize excess methionine cycle intermediates
Bibliographic record
Abstract
Glycine N -methyltransferase (GNMT) is the most abundant liver methyltransferase regulating the availability of the biological methyl donor, S -adenosylmethionine (SAM). Moreover, GNMT has been identified to be down-regulated in hepatocellular carcinoma (HCC). Despite its role in regulating SAM levels and association of its down-regulation with liver tumorigenesis, the impact of reduced GNMT on metabolic reprogramming before the manifestation of HCC has not been investigated in detail. Herein, we used 2 H/ 13 C metabolic flux analysis in conscious, unrestrained mice to test the hypothesis that the absence of GNMT causes metabolic reprogramming. GNMT-null (KO) mice displayed a reduction in blood glucose that was associated with a decline in both hepatic glycogenolysis and gluconeogenesis. The reduced gluconeogenesis was due to a decrease in liver gluconeogenic precursors, citric acid cycle fluxes, and anaplerosis and cataplerosis. A concurrent elevation in both hepatic SAM and metabolites of SAM utilization pathways was observed in the KO mice. Specifically, the increase in metabolites of SAM utilization pathways indicated that hepatic polyamine synthesis and catabolism, transsulfuration, and de novo lipogenesis pathways were increased in the KO mice. Of note, these pathways utilize substrates that could otherwise be used for gluconeogenesis. Also, this metabolic reprogramming occurs before the well-documented appearance of HCC in GNMT-null mice. Together, these results indicate that GNMT deletion promotes a metabolic shift whereby nutrients are channeled away from glucose formation toward pathways that utilize the elevated SAM.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".