Dual TCR-α Expression on Mucosal-Associated Invariant T Cells as a Potential Confounder of TCR Interpretation
Bibliographic record
Abstract
Abstract Mucosal-associated invariant T (MAIT) cells are innate-like T cells that are highly abundant in human blood and tissues. Most MAIT cells have an invariant TCRα-chain that uses T cell receptor α-variable 1-2 (TRAV1-2) joined to TRAJ33/20/12 and recognizes metabolites from bacterial riboflavin synthesis bound to the Ag-presenting molecule MHC class I related (MR1). Our attempts to identify alternative MR1-presented Ags led to the discovery of rare MR1-restricted T cells with non–TRAV1-2 TCRs. Because altered Ag specificity likely alters affinity for the most potent known Ag, 5-(2-oxopropylideneamino)-6-d-ribitylaminouracil (5-OP-RU), we performed bulk TCRα- and TCRβ-chain sequencing and single-cell–based paired TCR sequencing on T cells that bound the MR1-5-OP-RU tetramer with differing intensities. Bulk sequencing showed that use of V genes other than TRAV1-2 was enriched among MR1-5-OP-RU tetramerlow cells. Although we initially interpreted these as diverse MR1-restricted TCRs, single-cell TCR sequencing revealed that cells expressing atypical TCRα-chains also coexpressed an invariant MAIT TCRα-chain. Transfection of each non–TRAV1-2 TCRα-chain with the TCRβ-chain from the same cell demonstrated that the non–TRAV1-2 TCR did not bind the MR1-5-OP-RU tetramer. Thus, dual TCRα-chain expression in human T cells and competition for the endogenous β-chain explains the existence of some MR1-5-OP-RU tetramerlow T cells. The discovery of simultaneous expression of canonical and noncanonical TCRs on the same T cell means that claims of roles for non–TRAV1-2 TCR in MR1 response must be validated by TCR transfer-based confirmation of Ag specificity.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".