Migration Capacity of Thymic Regulatory T Cells can be Tuned by Expansion in Cytokine-Enriched Culture Conditions
Bibliographic record
Abstract
Introduction: Regulatory T cell (Tregs)-based therapy is a promising approach to treat allograft rejection. We have previously found that thymuses, routinely removed during pediatric cardiac surgery, are a potential source of therapeutic Tregs. To be effective, Tregs must express homing receptors for migration to inflammatory sites. For example, expression of the chemokine receptor CXCR3 on Tregs is essential to guide Tregs to locations of Th1-inflammation. We hypothesized that migration of thymic Tregs to Th1-inflammation could be fine-tuned by including cytokines in the expansion protocol. Methods: CD4 + CD25+ Tregs were isolated from pediatric thymuses by magnetic bead-separation. Tregs were expanded with artificial antigen-presenting cells, rapamycin and IL-2. The cells were re-stimulated after 7 days without rapamycin. For Th1-polarizing conditions, IL-12 and IFN-gamma were added to cultures either during the first, second or both rounds of stimulation. Results: Thymic Tregs cultured under Th1-polarizing conditions significantly increased CXCR3 and T-bet expression and showed >2-fold higher expansion capacity compared to Tregs cultured in neutral conditions. Expression of CXCR3 persisted even after removal of the polarizing cytokines. Importantly, Tregs cultured with Th1-inducing cytokines maintained a stable phenotype, including high FOXP3 expression and low TSDR methylation. Levels of other Treg-associated markers remained unchanged between neutrally and Th1-cultured Tregs at RNA- and protein-level. Th1-polarized Tregs did not acquire the ability to produce Th1-cell associated cytokines such as IL-2 or IFN-gamma and potently suppressed proliferation of total conventional T cells and Th1-effector T cells in vitro. In contrast to neutral cultures, expansion under Th1-conditions enabled thymic Tregs to migrate towards the CXCR3-specific chemokine CXCL10 in vitro. Conclusions: Expansion conditions of thymic Tregs can be manipulated to specifically and stably tailor the cells’ homing capacity. The ability to direct Tregs towards specific tissues or sites of inflammation may enable optimal targeting as a therapeutic in vivo. This means that Tregs could act in a more specific manner which would reduce pan-immunosuppression often associated with polyclonal Treg administration in animal models and early clinical trials.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".