Fine epitope mapping within the pathogenic thyroglobulin peptide 2340–2359: minimal epitopes retaining antigenicity across various MHC haplotypes are not necessarily immunogenic
Bibliographic record
Abstract
We have previously reported that the 20-mer peptide p2340 (amino acids 2340-2359), of human thyroglobulin (Tg) has the unique feature that it causes experimental autoimmune thyroiditis (EAT) in mouse strains bearing high-responder (HR) or low-responder (LR) MHC haplotypes in Tg-induced EAT. In this study, we have employed fine epitope mapping to examine whether this property of p2340 is the result of recognition of distinct or shared minimal T-cell epitopes in the context of HR or LR MHC class II molecules. Use of overlapping peptides showed that a core minimal 9-mer epitope (LTWVQTHIR, amino acids 2344-2352) was recognized by p2340-primed T cells from both HR (H2(k,s) ) and LR (H2(b,d) ) strains, whereas a second 9-mer epitope (HIRGFGGDP, amino acids 2350-2358) was antigenic only in H2(s) hosts. Truncation analysis of LTWVQTHIR and HIRGFGGDP peptides delineated them as the minimal epitopes recognized by p2340-primed T cells from the above strains. Subcutaneous challenge of all mouse strains with the 9-mer core peptide LTWVQTHIR in adjuvant elicited specific lymph node cell proliferative responses and mild EAT only in HR hosts, highlighting this sequence as a minimal pathogenic Tg peptide in EAT. The 9-mer peptide HIRGFGGDP was not found to be immunogenic in H2(s) hosts. These data demonstrate that minimal T-cell epitopes, defined as autoantigenic in hosts of various MHC haplotypes, are not intrinsically immunogenic. Activation of naive autoreactive T cells may require contributions from flanking residues within longer peptide sequences encompassing these epitopes.
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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".