Delineation of Five Thyroglobulin T Cell Epitopes with Pathogenic Potential in Experimental Autoimmune Thyroiditis
Bibliographic record
Abstract
Experimental autoimmune thyroiditis (EAT) is a T cell-mediated disease that can be induced in mice after challenge with thyroglobulin (Tg) or Tg peptides. To date, five pathogenic Tg peptides have been identified, four of which are clustered toward the C-terminal end. Because susceptibility to EAT is under control of H-2A(k) genes, we have used an algorithm-based approach to identify A(k)-binding peptides with pathogenic potential within mouse Tg. Eight candidate synthetic peptides, varying in size from 9 to 15 aa, were tested and five of those (p306, p1579, p1826, p2102, and p2596) were found to induce EAT in CBA/J (H-2(k)) mice either after direct challenge with peptide in adjuvant or by adoptive transfer of peptide-sensitized lymph node cells (LNCs) into naive hosts. These pathogenic peptides were immunogenic at the T cell level, eliciting specific LNC proliferative responses and IL-2 and/or IFN-gamma secretion in recall assays in vitro, but contained nondominant epitopes. All immunogenic peptides were confirmed as A(k) binders because peptide-specific LNC proliferation was blocked by an A(k)-specific mAb, but not by a control mAb. Peptide-specific serum IgG was induced only by p2102 and p2596, but these Abs did not bind to intact mouse Tg. This study reaffirms the predictive value of A(k)-binding motifs in epitope mapping and doubles the number of known pathogenic T cell determinants in Tg that are now found scattered throughout the length of this large autoantigen. This knowledge may contribute toward our understanding of the pathogenesis of autoimmune thyroiditis.
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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) | 0.000 | 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".