Specific Roles of Each TCR Hemichain in Generating Functional Chain-Centric TCR
Bibliographic record
Abstract
TCRα- and β-chains cooperatively recognize peptide-MHC complexes. It has been shown that a "chain-centric" TCR hemichain can, by itself, dictate MHC-restricted Ag specificity without requiring major contributions from the paired TCR counterchain. Little is known, however, regarding the relative contributions and roles of chain-centric and its counter, non-chain-centric, hemichains in determining T cell avidity. We comprehensively analyzed a thymically unselected T cell repertoire generated by transducing the α-chain-centric HLA-A*02:01(A2)/MART127-35 TCRα, clone SIG35α, into A2-matched and unmatched postthymic T cells. Regardless of their HLA-A2 positivity, a substantial subset of peripheral T cells transduced with SIG35α gained reactivity for A2/MART127-35. Although the generated A2/MART127-35-specific T cells used various TRBV genes, TRBV27 predominated with >10(2) highly diverse and unique clonotypic CDR3β sequences. T cells individually reconstituted with various A2/MART127-35 TRBV27 TCRβ genes along with SIG35α possessed a wide range (>2 log orders) of avidity. Approximately half possessed avidity higher than T cells expressing clone DMF5, a naturally occurring A2/MART127-35 TCR with one of the highest affinities. Importantly, similar findings were recapitulated with other self-Ags. Our results indicate that, although a chain-centric TCR hemichain determines Ag specificity, the paired counterchain can regulate avidity over a broad range (>2 log orders) without compromising Ag specificity. TCR chain centricity can be exploited to generate a thymically unselected Ag-specific T cell repertoire, which can be used to isolate high-avidity antitumor T cells and their uniquely encoded TCRs rarely found in the periphery because of tolerance.
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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".