Conversion of Tyrosine to the Inflammation-Associated Analog 3′-Nitrotyrosine at Either TCR- or MHC-Contact Positions Can Profoundly Affect Recognition of the MHC Class I-Restricted Epitope of Lymphocytic Choriomeningitis Virus Glycoprotein 33 by CD8 T Cells
Bibliographic record
Abstract
Immunohistochemical detection of increased levels of protein-associated nitrotyrosine has become widely used as a surrogate marker of in situ inflammation. However, the potential consequences of protein-associated nitrotyrosine formation in terms of cellular immune recognition has received surprisingly little attention. Using a well-defined I-E(K)-restricted epitope of pigeon cytochrome c, we previously demonstrated that conversion of a single tyrosine residue to nitrotyrosine can have a profound effect on recognition by CD4 T cells. In this study, we used the MHC class I-restricted epitope of lymphocytic choriomeningitis virus glycoprotein (gp33) to demonstrate that conversion of tyrosine to nitrotyrosine can also profoundly affect recognition of MHC class I-restricted epitopes. Conversion of the Y4 residue of the gp33 epitope to nitrotyrosine completely abrogated recognition by gp33-specific T cells from P14 TCR-transgenic mice. In contrast, CD8(+) T cells specific for "nitrated gp33" (NY-gp33) can be readily elicited in C57BL/6 mice after immunization with NY-gp33 peptide. Interestingly, T-T hybridomas specific for NY-gp33 peptide were found to fall into two distinct subsets, being specific for NY-gp33 presented in the context of either H-2D(b) or H-2K(b). This latter result is surprising in light of previous structural studies showing that Y4 comprises a critical TCR-contact residue when presented by H-2D(b) but that the same residue points downward into the peptide-binding groove of the MHC when presented by H-2K(b). Together, these results indicate that nitrotyrosine formation can impact T cell recognition both directly, through alteration of TCR-contact residues, or indirectly, through alterations in MHC-contact positions.
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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 | 0.001 |
| 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".