Human peripheral invariant natural killer T are adaptive lymphocytes, whose TCR CDR3β regulates their antigen specificity and reactivity to avoid harmful autoimmune responses (BA8P.124)
Bibliographic record
Abstract
Abstract Human invariant natural killer T (iNKT) cells carry a sole variable element, complementarity determining region 3β (CDR3β) in their T cell receptors (TCRs). It has been widely accepted that iNKT cells recognize foreign and self-ligands via CD1d in a pattern recognition-like innate-type manner. Here, we have created a thymically unselected human iNKT Vβ11 TCR repertoire and isolated a series of iNKT TCRs with ultra-high to low autoreactivity. We have found three distinctive CDR3β sequence motifs that dictate high iNKT TCR autoreactivity. In vitro and in vivo studies demonstrated that highly autoreactive iNKT cells carry innate-type TCRs, recognize CD1d molecules in a ligand-independent manner and evoke severe systemic autoimmune responses within a few hours upon adoptive transfer. In contrast, iNKT cells with intermediate to low autoreactivity express adaptive-type TCRs, which selectively recognize self-ligands and induce tissue-specific moderate autoimmune responses in vivo. High throughput CDR3β sequencing revealed that the frequency of highly autoreactive human iNKT cells positive for all 3 motifs was significantly reduced among iNKT cells in the periphery compared to the thymus. We conclude that intermediately and lowly autoreactive iNKT cells which prevail in the human periphery are adaptive lymphocytes and that the sole adaptive CDR3β sequences regulate the antigen specificity and reactivity of iNKT cells to avoid harmful autoimmune responses.
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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) | 0.002 | 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".