Nitration of Tyrosine 92 Mediates the Activation of Rat Microsomal Glutathione S-Transferase by Peroxynitrite
Bibliographic record
Abstract
There is increasing evidence that protein function can be modified by nitration of tyrosine residue(s), a reaction catalyzed by proteins with peroxidase activity, or that occurs by interaction with peroxynitrite, a highly reactive oxidant formed by the reaction of nitric oxide with superoxide. Although there are numerous reports describing loss of function after treatment of proteins with peroxynitrite, we recently demonstrated that the microsomal glutathione S-transferase 1 is activated rather than inactivated by peroxynitrite and suggested that this could be attributed to nitration of tyrosine residues rather than to other effects of peroxynitrite. In this report, the nitrated tyrosine residues of peroxynitrite-treated microsomal glutathione S-transferase 1 were characterized by mass spectrometry and their functional significance determined. Of the seven tyrosine residues present in the protein, only those at positions 92 and 153 were nitrated after treatment with peroxynitrite. Three mutants (Y92F, Y153F, and Y92F, Y153F) were created using site-directed mutagenesis and expressed in LLC-PK1 cells. Treatment of the microsomal fractions of these cells with peroxynitrite resulted in an approximately 2-fold increase in enzyme activity in cells expressing the wild type microsomal glutathione S-transferase 1 or the Y153F mutant, whereas the enzyme activity of Y92F and double site mutant was unaffected. These results indicate that activation of microsomal glutathione S-transferase 1 by peroxynitrite is mediated by nitration of tyrosine residue 92 and represents one of the few examples in which a gain in function has been associated with nitration of a specific tyrosine residue.
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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".