TCR Bias and Affinity Define Two Compartments of the CD1b–Glycolipid-Specific T Cell Repertoire
Bibliographic record
Abstract
Current views emphasize TCR diversity as a key feature that differentiates the group 1 (CD1a, CD1b, CD1c) and group 2 (CD1d) CD1 systems. Whereas TCR sequence motifs define CD1d-reactive NKT cells, the available data do not allow a TCR-based organization of the group 1 CD1 repertoire. The observed TCR diversity might result from donor-to-donor differences in TCR repertoire, as seen for MHC-restricted T cells. Alternatively, diversity might result from differing CD1 isoforms, Ags, and methods used to identify TCRs. Using CD1b tetramers to isolate clones recognizing the same glycolipid, we identified a previously unknown pattern of V gene usage (TRAV17, TRBV4-1) among unrelated human subjects. These TCRs are distinct from those present on NKT cells and germline-encoded mycolyl lipid-reactive T cells. Instead, they resemble the TCR of LDN5, one of the first known CD1b-reactive clones that was previously thought to illustrate the diversity of the TCR repertoire. Interdonor TCR conservation was observed in vitro and ex vivo, identifying LDN5-like T cells as a distinct T cell type. These data support TCR-based organization of the CD1b repertoire, which consists of at least two compartments that differ in TCR sequence motifs, affinity, and coreceptor expression.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".