Selective inhibition of MR1-restricted T cell activation by a novel MR1-targeting nanobody
Bibliographic record
Abstract
Abstract MR1 is a non-polymorphic, ubiquitously expressed, MHC class I-like antigen-presenting molecule that presents small-molecule metabolites to T cells. Studies have shown that MR1 plays a role in microbial infection, inflammation, and tumor immunity. The antigens it presents include metabolites of microbial and self-origin as well as small-molecule drugs and form stable complexes with MR1 that are displayed on the cell surface to activate T cells. However, unlike classical MHC I and II molecules, the fundamental biology of MR1 remains poorly understood, particularly the mechanisms governing antigen loading and intracellular trafficking. This knowledge gap is largely due to the lack of molecular tools available to precisely manipulate MR1 function. In this study, we describe a high-affinity (1.6 nM K D ) anti-MR1 nanobody, MR1Nb1. We characterize the binding of this nanobody including affinity by ELISA and kinetics by BLI. Crucially, we map the binding epitope of MR1Nb1 on MR1 by HDX-MS, providing key insights into the mechanism through which it blocks MR1T cell activation. In functional assays MR1Nb1 effectively and specifically blocks MAIT cell activation by cells infected with M. tuberculosis or treated with M. smegmatis supernatant. This nanobody represents a unique and versatile tool for the field, as it can be produced inexpensively and expressed intracellularly within antigen-presenting cells. Hence, our study provides a powerful new molecular probe for dissecting the mechanistic underpinnings of MR1 biology and uncover its broader roles in immunity.
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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 | 0.001 | 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".