Mouse and Human CD1d-Self-Lipid Complexes Are Recognized Differently by Murine Invariant Natural Killer T Cell Receptors
Bibliographic record
Abstract
Invariant natural killer T (iNKT) cells recognize self-lipids presented by CD1d through characteristic TCRs, which mainly consist of the invariant Vα14-Jα18 TCRα chain and Vβ8.2, 7 or 2 TCRβ chains with hypervariable CDR3β sequences in mice. The iNKT cell-CD1d axis is conserved between humans and mice, and human CD1d reactivity of murine iNKT cells have been described. However, the detailed differences between the recognition of human and mouse CD1d bound to various self-lipids by mouse iNKT TCRs are largely unknown. In this study, we generated a de novo murine iNKT TCR repertoire with a wider range of autoreactivity compared with that of naturally occurring peripheral iNKT TCRs. Vβ8.2 mouse iNKT TCRs capable of recognizing the human CD1d-self-lipid tetramer were identified, although such clones were not detectable in the Vβ7 or Vβ2 iNKT TCR repertoire. In line with previously reports, clonotypic Vβ8.2 iNKT TCRs with unique CDR3β loops did not discriminate among lipids presented by mouse CD1d. Unexpectedly, however, these iNKT TCRs showed greater ligand selectivity toward human CD1d presenting the same lipids. Our findings demonstrated that the recognition of mouse and human CD1d-self-lipid complexes by murine iNKT TCRs is not conserved, thereby further elucidating the differences between cognate and cross-species reactivity of self-antigens by mouse iNKT 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 | 0.000 |
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".