Antigen Specificity of Type I NKT Cells Is Governed by TCR β-Chain Diversity
Bibliographic record
Abstract
NKT cells recognize lipid-based Ags presented by CD1d. Type I NKT cells are often referred to as invariant owing to their mostly invariant TCR α-chain usage (Vα14-Jα18 in mice, Vα24-Jα18 in humans). However, these cells have diverse TCR β-chains, including Vβ8, Vβ7, and Vβ2 in mice and Vβ11 in humans, joined to a range of TCR Dβ and Jβ genes. In this study, we demonstrate that TCR β-chain composition can dramatically influence lipid Ag recognition in an Ag-dependent manner. Namely, the glycolipids α-glucosylceramide and isoglobotrihexosylceramide were preferentially recognized by Vβ7(+) NKT cells from mice, whereas the α-galactosylceramide analog OCH, with a truncated sphingosine chain, was preferentially recognized by Vβ8(+) NKT cells from mice. We show that the influence of the TCR β-chain is due to a combination of Vβ-, Jβ-, and CDR3β-encoded residues and that these TCRs can recapitulate the selective Ag reactivity in TCR-transduced cell lines. Similar observations were made with human NKT cells where different CDR3β-encoded residues determined Ag preference. These findings indicate that NKT TCR β-chain diversity results in differential and nonhierarchical Ag recognition by these cells, which implies that some Ags can preferentially activate type I NKT cell subsets.
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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".