Ammonia induces MK‐801‐sensitive nitration and phosphorylation of protein tyrosine residues in rat astrocytes
Bibliographic record
Abstract
Astrocytes play a key role in the pathogenesis of ammonia-induced neurotoxicity and hepatic encephalopathy. As shown here, ammonia induces protein tyrosine nitration in cultured rat astrocytes, which is sensitive to the N-methyl-D-aspartate (NMDA) receptor antagonist MK-801. A similar pattern of nitrated proteins is produced by NMDA. Ammonia-induced tyrosine nitration depends on a rise in [Ca2+]i, IkB degradation, and NO synthase (iNOS) induction, which are prevented by MK-801 and the intracellular Ca2+ chelator 1,2-bis(o-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid (BAPTA-AM). Moreover, the increase in tyrosine nitration is blunted by L-NMMA, 1400W, uric acid, Cu, Zn-superoxide dismutase/catalase treatment, and methionine-sulfoximine, which indicate the involvement of reactive nitrogen intermediates and intracellular glutamine accumulation. Such reactive nitrogen intermediates additionally mediate ammonia-induced phosphorylation of the MAP-kinases Erk-1/Erk-2 and p38MAPK. Among the proteins, which are tyrosine -nitrated by ammonia, glyceraldehyde-3-phosphate dehydrogenase, the peripheral-type benzodiazepine receptor, Erk-1, and glutamine synthetase are identified. Ammonia-induced nitration of glutamine synthetase is associated with a loss of enzymatic activity. Astroglial protein tyrosine nitration is found in brains from rats after acute ammonia-intoxication or after portacaval anastomosis, indicating the in vivo relevance of the present findings. The production of reactive nitrogen intermediates and protein tyrosine nitration may alter astrocyte function and contribute to ammonia neurotoxicity.
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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".