Eras of designer Tregs: Harnessing synthetic biology for immune suppression
Bibliographic record
Abstract
regulatory T cells (Tregs) have been firmly established as a critical cell type for regulating immune homeostasis through a plethora of mechanisms. Due to their immunoregulatory power, delivery of polyclonal Tregs has been explored as a therapy to dampen inflammation in the settings of transplantation and autoimmunity. Evidence shows that Treg therapy is safe and well-tolerated, but efficacy remains undefined and could be limited by poor persistence in vivo and lack of antigen specificity. With the advent of new genetic engineering tools, it is now possible to create bespoke "designer" Tregs that not only overcome possible limitations of polyclonal Tregs but also introduce new features. Here, we review the development of designer Tregs through the perspective of three 'eras': (1) the era of FOXP3 engineering, in which breakthroughs in the biological understanding of this transcription factor enabled the conversion of conventional T cells to Tregs; (2) the antigen-specificity era, in which transgenic T-cell receptors and chimeric antigen receptors were introduced to create more potent and directed Treg therapies; and (3) the current era, which is harnessing advanced genome-editing techniques to introduce and refine existing and new engineering approaches. The year 2022 marked the entry of "designer" Tregs into the clinic, with exciting potential for application and efficacy in a wide variety of immune-mediated 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".