The molecular basis underlying T cell specificity towards citrullinated epitopes presented by HLA-DR4
Bibliographic record
Abstract
Abstract CD4+ T cells recognising citrullinated self-epitopes presented by HLA-DRB1 bearing the shared susceptibility epitope (SE) are implicated in rheumatoid arthritis (RA). However, the underlying T cell receptor (TCR) determinants of epitope specificity towards distinct citrullinated peptide antigens, including vimentin-64cit59-71 and α-enolase-15cit10-22 remain unclear. Using HLA-DR4-tetramers, we examine the T cell repertoire in HLA-DR4 transgenic mice and observe biased TRAV6 TCR gene usage across these two citrullinated epitopes which matches with TCR bias previously observed towards the fibrinogen β−74cit69-81 epitope. Moreover, shared TRAV26-1 gene usage is evident in four α-enolase-15cit10-22 reactive T cells in three human samples. Crystal structures of mouse TRAV6+ and human TRAV26-1+ TCR-HLA-DR4 complexes presenting vimentin-64cit59-71 and α-enolase-15cit10-22, respectively, show three-way interactions between the TCR, SE, citrulline, and the basis for the biased selection of TRAV genes. Position 2 of the citrullinated epitope is a key determinant underpinning TCR specificity. Accordingly, we provide a molecular basis of TCR specificity towards citrullinated 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) | 0.002 | 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".