Membrane-bound IL-2 improves the expansion, survival, and phenotype of CAR Tregs and confers resistance to calcineurin inhibitors
Bibliographic record
Abstract
Background Regulatory T cells (Tregs) play an important role in the maintenance of immune homeostasis and the establishment of immune tolerance. Since Tregs do not secrete endogenous IL-2, they are especially dependent on external IL-2. IL-2 deficiency leads to lower Treg numbers, instability of the Treg phenotype and loss of immune regulation. After organ transplantation, patients are treated with calcineurin inhibitors (CNIs), which further limits available IL-2. Application of low-dose IL-2 expands Tregs but also activates NK and CD8+ T cells. It was recently shown that graft-specific Tregs recognizing mismatched MHC I molecules via a chimeric antigen receptor were far more potent than polyclonal Tregs in the regulation of immune responses after solid organ transplantation in a humanized mouse model. Methods Therefore, our aim was to enhance the function and stability of transferred CAR-Tregs via expression of membrane-associated IL-2 (mbIL-2). Results mbIL-2 promoted higher survival, phenotypic stability, and function among CAR-Tregs than observed in clinical trials. The cells were also more stable under inflammatory conditions. In a preclinical humanized mouse model, we demonstrated that mbIL-2 CAR Tregs survive better in the Treg niche than control CAR Tregs and are even resistant to CNI therapy without affecting other Tregs, thus acting mainly in cis. Discussion The functional and phenotypic improvements observed after membrane-attached IL-2 expression in CAR-Tregs will be important step for enhancing CAR-Treg therapies currently being tested in clinical trials for use after kidney and liver transplantation as well as in autoimmune diseases.
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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 | 0.000 |
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".