Gene editing of <i>CD3 epsilon</i> gene to redirect regulatory T cells for adoptive T cell transfer
Bibliographic record
Abstract
I. Abstract Adoptive transfer of regulatory T cells (Tregs) is a promising strategy to combat immunopathologies in transplantation and autoimmune diseases. Antigen-specific Tregs are more effective in modulating undesired immune reactions, but their low frequency in peripheral blood poses challenges for manufacturing and their clinical application. Chimeric antigen receptors (CARs) have been used to redirect the specificity of Tregs, employing retroviral vectors. However, retroviral gene transfer is costly, time consuming, and raises safety issues. Here, we explored non-viral gene editing to redirect Tregs with CARs, using HLA-A2-specific constructs for proof-of-concept studies in transplantation models. We introduce a virus-free CRISPR-Cas12a approach to integrate an antigen-binding domain into the CD3 epsilon ( CD3ε ) gene, generating Tregs expressing a T cell receptor fusion construct (TruC). These CD3ε -TruC Tregs exhibit potent antigen-dependent activation while maintaining responsiveness to TCR/CD3 stimulation. This enables preferential enrichment of TruC-redirected Tregs via repetitive CD3/CD28-stimulation in a GMP-compatible expansion system. Non-viral gene edited CD3ε -TruC Tregs retained their phenotypic, epigenetic, and functional identity. In a humanized mouse model, HLA-A2-specific CD3ε -TruC Tregs demonstrate superior protection of allogeneic HLA-A2 + skin grafts from rejection compared to polyclonal Tregs. This approach provides a pathway for developing clinical-grade CD3ε -TruC-based Treg cell products for transplantation immunotherapy and other immunopathologies.
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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) | 0.002 | 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".