Tailoring the homing capacity of human Tregs for directed migration to sites of Th1-inflammation or intestinal regions
Bibliographic record
Abstract
Cell-based therapy with CD4 + FOXP3 + regulatory T cells (Tregs) is a promising strategy to limit organ rejection and graft-vs-host disease. Ongoing clinical applications have yet to consider how human Tregs could be modified to direct their migration to specific inflammation sites and/or tissues for more targeted immunosuppression. We show here that stable, homing-receptor-tailored human Tregs can be generated from thymic Tregs isolated from pediatric thymus or adult blood. To direct migration to Th1-inflammatory sites, addition of interferon-γ and IL-12 during Treg expansion produced suppressive, epigenetically stable CXCR3 + TBET + FOXP3 + T helper (Th)1-Tregs. CXCR3 remained expressed after injection in vivo and Th1-Tregs migrated efficiently towards CXCL10 in vitro. To induce tissue-specific migration, addition of retinoic acid (RA) during Treg expansion induced expression of the gut-homing receptors α4β7-integrin and CCR9. FOXP3 + RA-Tregs had elevated expression of the functional markers latency-associated peptide and glycoprotein A repetitions predominant, increased suppressive capacity in vitro and migrated efficiently to healthy and inflamed intestine after injection into mice. Homing-receptor-tailored Tregs were epigenetically stable even after long-term exposure to inflammatory conditions, suppressive in vivo and characterized by Th1- or gut-homing-specific transcriptomes. Tailoring human thymic Treg homing during in vitro expansion offers a new and clinically applicable approach to improving the potency and specificity of Treg therapy.
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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 | 0.000 |
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".