Selective Nitration of Histone Tyrosine Residues in Vivo in Mutatect Tumors
Bibliographic record
Abstract
Nitric oxide-derived reactive species have been implicated in many disorders. Protein nitrotyrosine is often used as a stable marker of these reactive species. Using immunohistochemistry, we have previously detected nitrotyrosine in murine Mutatect tumors, where neutrophils are the principal source of nitric oxide. We now report on the identification of several prominent nitrotyrosine-containing proteins. Using Western blot analysis, nitrotyrosine in higher molecular mass proteins (>20 kDa) was detected in tumors containing a high number of neutrophils but not in tumors with fewer neutrophils. Staining for nitrotyrosine was consistently seen in low molecular mass proteins (< or =15 kDa), regardless of the level of neutrophils. Protein nitrotyrosine was not seen in Mutatect cells growing in vitro. Treatment with nitric oxide donors produced nitration of < or =15-kDa proteins, but only after extended periods. These small proteins, both from tumors and cultured cells, were identified by mass spectrometry to be histones. Only a subset of tyrosine residues was nitrated. Selective nitration may reflect differential accessibility of different tyrosine residues and the influence of neighboring residues within the nucleosome. The prominence of histone nitration may reflect its relative stability, making this post-translational modification a potentially useful marker of extended exposure of cells or tissues to nitric oxide-derived reactive species.
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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".