Generation and molecular recognition of melanoma-associated antigen-specific human γδ T cells
Bibliographic record
Abstract
Antigen recognition by T cells bearing αβ T cell receptors (TCRs) is restricted by major histocompatibility complex (MHC). However, how antigens are recognized by T cells bearing γδ TCRs remains unclear. Although γδ T cells can recognize nonclassical MHC, it is generally thought that recognition of antigens is not MHC restricted. Here, we took advantage of an in vitro system to generate antigen-specific human T cells and show that melanoma-associated antigens, MART-1 and gp100, can be recognized by γδ T cells in an MHC-restricted fashion. Cloning and transferring of MART-1-specific γδ TCRs restored the specific recognition of the initial antigen MHC/peptide reactivity and conferred antigen-specific functional responses. A crystal structure of a MART-1-specific γδ TCR, together with MHC/peptide, revealed distinctive but similar docking properties to those previously reported for αβ TCRs, recognizing MART-1 on HLA-A*0201. Our work shows that antigen-specific and MHC-restricted γδ T cells can be generated in vitro and that MART-1-specific γδ T cells can also be found and cloned from the naïve repertoire. These findings reveal that classical MHC-restricted human γδ TCRs exist in the periphery and have the potential to be used in developing of new TCR-based immunotherapeutic approaches.
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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 | 0.001 |
| 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".