Variable influences of iodine on the T‐cell recognition of a single thyroglobulin epitope
Bibliographic record
Abstract
We have previously shown that iodotyrosyl formation within certain innocuous thyroglobulin (Tg) peptides confers on them immunopathogenic properties. In this report, we generated a panel of T-cell hybridoma clones specific for the immunogenic 16 mer Tg peptide p179 (amino acids 179-94) or its iodinated analogue (I-p179), with a view to examining the effects of a single iodine atom at the Y192 amino acid residue on T-cell recognition. We found that the peptide p179 was subdominant, and its binding to both A(k) and E(k) molecules was not significantly influenced by iodine. T-cell receptor (TCR) engagement was unaffected by the bulky iodine atom in two clones that responded to both analogues but it was sterically hindered in two other clones that recognized only p179. One clone was reactive only to I-p179, suggesting that the iodine atom is an integral part of its TCR ligand. Truncation analysis localized the determinant seen by all clones within the 11 mer peptide p184 (amino acids 184-194), suggesting that the cross-reactive clones were not activated by a minimal epitope lacking Y192 and that the negative influence of iodine was not the result of a flanking residue effect. These results demonstrate, at the clonal level, variable influences of a single iodine atom on the recognition of a single Tg peptide. Iodination of tyrosyl-containing, immunopathogenic Tg peptides may have unpredictable effects at the polyclonal level, depending on the extent of iodination at the particular site, and the relative number or effector function of autoreactive T-cell clones that are switched on or off by the neoantigenic determinant.
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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".