A single amino acid residue of CDR3β alters the antigenicity and autoreactivity of human invariant natural killer T cell receptors (IRM7P.707)
Bibliographic record
Abstract
Abstract Detection of human invariant natural killer T (iNKT) cells relies on staining for their T cell receptor (TCR), invariant Vα24 TCRα chain paired with semi-variant Vβ11 TCRβ chain, or α-GalCer-CD1d tetramer. It has been assumed that all Vα24+ iNKT cells are stained with similar intensity by anti-Vα24 mAbs, clones C15 and 6B11, regardless of the hypervariable Vβ11 CDR3β sequences. By analyzing a large panel of autoreactive human iNKT TCRs, we found that TCRs encoding CASR at the start of CDR3β were stained with significantly lower intensity by C15 than those encoding CASS. Substitution of S to R at the fourth CDR3β residue was sufficient to diminish staining intensity by anti-Vα24 mAbs. Interestingly, the autoreactivity was also reduced in the TCRs encoding CASR. Importantly, α-GalCer tetramer positive population of human primary T cells presented low and high intensity subsets when stained with anti-Vα24 mAbs. Vβ11 TCRβ chains were cloned from each sorted population, and the Vα24 low population contained a significantly higher frequency of Vβ11 TCRs encoding CASR compared to the Vα24 high population. Therefore, human iNKT TCRs encoding CASR may falsely be negative when stained with anti-Vα24 mAbs, which underestimate the frequency of bona fide Vα24+ iNKT cells. Our data demonstrate that the fourth CDR3β amino acid residue affects iNKT TCR autoreactivity and binding of anti-Vα24 mAbs, suggesting that the CDR3β sequence alters the structural conformation of the Vα24 TCRα chain.
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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".