CD8+ TCR Repertoire Formation Is Guided Primarily by the Peptide Component of the Antigenic Complex
Bibliographic record
Abstract
CD8(+) T cells recognize infected or dysregulated cells via the clonotypically expressed αβ TCR, which engages Ag in the form of peptide bound to MHC class I (MHC I) on the target cell surface. Previous studies have indicated that a diverse Ag-specific TCR repertoire can be beneficial to the host, yet the determinants of clonotypic diversity are poorly defined. To better understand the factors that govern TCR repertoire formation, we conducted a comprehensive clonotypic analysis of CD8(+) T cell populations directed against epitopes derived from EBV and CMV. Neither pathogen source nor the restricting MHC I molecule were linked with TCR diversity; indeed, both HLA-A and HLA-B molecules were observed to interact with an overlapping repertoire of expressed TRBV genes. Peptide specificity, however, markedly impacted TCR diversity. In addition, distinct peptides sharing HLA restriction and viral origin mobilized TCR repertoires with distinct patterns of TRBV gene usage. Notably, no relationship was observed between immunodominance and TCR diversity. These findings provide new insights into the forces that shape the Ag-specific TCR repertoire in vivo and highlight a determinative role for the peptide component of the peptide-MHC I complex on the molecular frontline of CD8(+) T cell-mediated immune surveillance.
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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".