Mouse mucosal-associated invariant T cell receptor recognition of MR1 presenting the vitamin B metabolite, 5-(2-oxopropylideneamino)-6-d-ribitylaminouracil
Bibliographic record
Abstract
Mucosal-associated invariant T (MAIT) cells can elicit immune responses against riboflavin-based antigens presented by the evolutionary conserved MHC Class I related-1 protein, MR1. While we have an understanding of the structural basis of human MAIT cell receptor (TCR) recognition of human MR1 presenting a variety of ligands, how the semi-invariant mouse MAIT TCR binds mouse MR1-ligand remains unknown. Here we determine the crystal structures of two mouse TRAV1-TRBV13-2 + MAIT TCR-MR1-5-OP-RU ternary complexes, whose TCRs differ only in the composition of their CDR3β loops. These mouse MAIT TCRs mediate high affinity interactions with mouse MR1-5-OP-RU and cross-recognise human MR1-5-OP-RU. Similarly, a human MAIT TCR could bind mouse MR1-5-OP-RU with high affinity. This cross-species recognition indicates the evolutionary conserved nature of this MAIT TCR-MR1 axis. Comparing crystal structures of the mouse versus human MAIT TCR-MR1-5-OP-RU complexes provides structural insight into the conserved nature of this MAIT TCR-MR1 interaction, and conserved specificity for the microbial antigens, whereby key germline-encoded interactions required for MAIT activation are maintained. This is an important consideration for the development of MAIT cell-based therapeutics that will rely on preclinical mouse models of disease.
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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".