γδ T cell receptor recognition of CD1d in a lipid-independent manner
Bibliographic record
Abstract
Abstract The monomorphic antigen-presenting molecule CD1d presents lipid antigens to both αβ and γδ T cells. While type I natural killer T cells (NKT) display exquisite specificity for CD1d presenting α-galactosylceramide (α-GalCer), the extent of lipid specificity exhibited by CD1d-restricted γδ T cells remains unclear. Here, we demonstrate that human γδ T cell receptors (TCRs) can recognise CD1d in either a lipid-dependent or lipid-independent manner with weak to moderate affinity. Using small-angle X-Ray scattering, we find that γδ TCR-CD1d binding modality is conserved across distinct CD1d-restricted TCRs. In functional assays, CD1d γδ TCR affinity was a poor predictor of γδ T cell line activation. Moreover, CD1d presenting endogenous lipids was sufficient to stimulate T cell activation and induce γδ TCR-CD3 clustering and phosphorylation in a dose-dependent manner. Elongation of the γδ TCR-CD3 complex by the inclusion of the Cγ2 and Cγ3 -encoded constant domains perturbed cellular activation whilst maintaining the ability to form functional γδ TCR clusters. The crystal structure of a Vδ1 γδ + TCR-CD1d complex showed that the γδ TCR sat atop of the CD1d antigen-binding cleft but made no contacts with the presented lipid antigen. These findings provide a molecular basis for lipid-independent CD1d recognition by γδ TCRs.
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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 | 0.001 |
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".