Exploitation of CD3ζ to enhance TCR expression levels and antigen-specific T cell function
Bibliographic record
Abstract
The expression levels of TCRs on the surface of human T cells define the avidity of TCR-HLA/peptide interactions. In this study, we have explored which components of the TCR-CD3 complex are involved in determining the surface expression levels of TCRs in primary human T cells. The results show that there is a surplus of endogenous TCR α/β chains that can be mobilised by providing T cells with additional CD3γ,δ,ε,ζ chains, which leads to a 5-fold increase in TCR α/β surface expression. The analysis of individual CD3 chains revealed that provision of additional ζ chain alone was sufficient to achieve a 3-fold increase in endogenous TCR expression. Similarly, CD3ζ also limits the expression levels of exogenous TCRs transduced into primary human T cells. Interestingly, transduction with TCR plus CD3ζ not only increased surface expression of the introduced TCR, but it also reduced mispairing with endogenous TCR chains, resulting in improved antigen-specific function. TCR reconstitution experiments in HEK293 cells that do not express endogenous TCR or CD3 showed that TCRα/β and all four CD3 chains were required for optimal surface expression, while in the absence of CD3ζ the TCR expression was reduced by 50%. Together, the data show that CD3ζ is a key regulator of TCR expression levels in human T cells, and that gene transfer of exogenous TCR plus CD3ζ improved TCR surface expression, reduced TCR mispairing and increased antigen-specific function.
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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 | 0.001 |
| 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".