Forced Fox-P3 expression can improve the safety and antigen-specific function of engineered regulatory T cells
Bibliographic record
Abstract
Regulatory T cells (Treg) are potent inhibitors of autoreactive T cells. The intracellular transcription factor FoxP3 controls the expression levels of a diverse set of genes and plays a critical role in programming functional Tregs. Although, antigen-specific Tregs are more potent than polyclonal Tregs in treating ongoing autoimmunity, phenotype plasticity associated with loss of FoxP3 expression in Tregs can lead to the conversion into antigen-specific effector T cells which might exacerbate autoimmune pathology. In this study, we designed a retroviral vector driving the expression of FoxP3 and a human HLA-DR-restricted TCR from the same promoter. Transduction of purified human Tregs revealed that all TCR-positive cells had elevated levels of FoxP3 expression, increased CD25 and CTLA4 expression and potent suppressive function. Elevated FoxP3 expression did not impair the in vitro expansion of engineered Tregs. Adoptive transfer into HLA-DR transgenic mice revealed that FoxP3+TCR engineered Tregs showed long-term persistence with stable FoxP3 and TCR expression. In contrast, adoptive transfer of Tregs engineered with TCR only resulted in the accumulation of TCR-positive, FoxP3-negative T cells which displayed antigen-specific effector function when stimulated with the TCR-recognised peptides. Our data indicate that forced expression of FoxP3 can prevent accumulation of antigen-specific effector T cells without impairing the engraftment and persistence of engineered Tregs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".