Inhibitory effects on HLA‐DR1‐specific T‐cell activation by influenza virus haemagglutinin‐derived peptides
Bibliographic record
Abstract
Collagen (CII) 263-272 peptide, an autoantigen in rheumatoid arthritis, is a specific human leukocyte antigen (HLA)-DR1/4-binding peptide recognized by T-cell receptors (TCR). The affinity of influenza virus haemagglutinin (HA) 306-318 peptide for the antigen-binding groove of HLA-DR1/4 molecules is higher than that of CII263-272. The HLA-DR1/4-binding residues of HA306-318 are located in the region 308-317. Altered HA308-317 peptides with substitutions of TCR-contact residues may inhibit HLA-DR1/4-specific T-cell activation by blocking the antigen-binding site of HLA-DR1/4 molecules. To evaluate the role of altered HA308-317 peptides in HLA-DR1-restricted T-cell activation, we synthesized three altered HA308-317 peptides. The specific binding of altered HA308-317 peptides to HLA-DR1 molecules was examined using flow cytometry. Effects of altered HA308-317 peptides on HLA-DR1-specific T-cell hybridoma were studied by measuring T-cell proliferation and surface expression of CD69 or CD25. The results showed that altered HA308-317 peptides were able to bind to HLA-DR1 molecules and competed with CII263-272 or wildtype HA308-317 peptide. Compared with wildtype CII263-272 or HA308-317, altered HA308-317 peptides did not stimulate significant T-cell proliferation and CD69 or CD25 expression. Furthermore, the altered HA308-317 peptides inhibited HLA-DR1-specific T-cell activation induced by CII263-272 or wildtype HA308-317 peptide, which may suggest an effective therapeutic strategy in inhibition of HLA-DR1-specific T-cell responses in autoimmunity.
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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".