Fourth generation CAR Tregs with <i>PDCD1</i> -driven IL-10 have enhanced suppressive function
Bibliographic record
Abstract
ABSTRACT The potency of regulatory T cell (Treg) therapy has been transformed through use of chimeric antigen receptors (CAR). However, to date, CAR Treg therapy has not achieved long-lasting tolerance in mouse models, suggesting that additional engineering is required to unlock the full potential of these cells. We previously found that human Tregs produce minimal amounts of IL-10 and have a limited capacity to control innate immunity in comparison to type I regulatory (Tr1) cells. Seeking to create “hybrid” CAR Tregs that were engineered with Tr1-like properties, we examined whether the PDCD1 locus could be exploited to endow Tregs with the ability to secrete high levels of IL-10 in a CAR-regulated manner. CRISPR-mediated PD1-deletion increased the activation potential of CAR Tregs without compromising in vivo stability. Knock-in of IL10 under control of the PD1 promoter facilitated CAR-mediated secretion of IL-10 in large quantities, and improved CAR Treg function, as determined by significant inhibition of dendritic cell antigen presentation and enhanced suppression of alloantigen- and islet autoantigen-specific T cells. Overall, CRISPR-mediated engineering to simultaneously remove an inhibitory signal and enhance suppressive mechanisms is a new approach to enhance the therapeutic potency of CAR Tregs.
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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) | 0.003 | 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".