Metabolic insights into the hepatoprotective role of N-acetylcysteine in mouse liver
Bibliographic record
Abstract
The hepatoprotective mechanisms of N-acetylcysteine (NAC) in non-acetaminophen-induced liver injury have not been studied in detail. We investigated the possibility that NAC could affect key pathways of hepatocellular metabolism with or without changes in glutathione (GSH) synthesis. Hepatocellular metabolites and high-energy phosphates were quantified from mouse liver extracts by 1H- and 31P-NMR (nuclear magnetic resonance) spectroscopy. 13C-NMR-isotopomer analysis was used to measure [U-13C]glucose metabolism through pyruvate dehydrogenase (PDH) and pyruvate carboxylase (PC). NAC (150-1,200 mg/kg) increased liver concentrations of GSH from 8.60 +/- 0.48 to a maximum of 12.95 +/- 1.03 micromol/g ww, whereas hypotaurine (HTau) concentrations increased from 0.05 +/- 0.02 to 9.95 +/- 1.12 micromol/g ww. The limited capacity of NAC to increase GSH synthesis was attributed to impaired glucose metabolism through PC. However, 300 mg/kg NAC significantly increased the fractional 13C-enrichment in Glu (from 2.08% +/- 0.26% to 4.00% +/- 0.44%) synthesized through PDH, a key enzyme for mitochondrial energy metabolism. This effect could be uncoupled from GSH synthesis and was associated with the prevention of liver injury induced by tert-butylhydroperoxide and 3-nitropropionic acid. In conclusion, NAC (1) has a limited capacity to elevate GSH synthesis; (2) increases HTau formation linearly; and (3) improves mitochondrial tricarboxylic acid (TCA) cycle metabolism by stimulation of carbon flux through PDH. This latter effect is independent of the capacity of NAC to replete GSH stores. These metabolic actions, among other yet unknown effects, are critical for NAC's therapeutic value and should be taken into account when deciding on a wider use of NAC.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".