Innate Control of Tissue-Reparative Human Regulatory T Cells
Bibliographic record
Abstract
Abstract Regulatory T cell (Treg) therapy is a potential curative approach for a variety of immune-mediated conditions, including autoimmunity and transplantation, in which there is pathological tissue damage. In mice, IL-33R (ST2)–expressing Tregs mediate tissue repair by producing the growth factor amphiregulin, but whether similar tissue-reparative Tregs exist in humans remains unclear. We show that human Tregs in blood and multiple tissue types produced amphiregulin, but this was neither a unique feature of Tregs nor selectively upregulated in tissues. Human Tregs in blood, tonsil, synovial fluid, colon, and lung tissues did not express ST2, so ST2+ Tregs were engineered via lentiviral-mediated overexpression, and their therapeutic potential for cell therapy was examined. Engineered ST2+ Tregs exhibited TCR-independent, IL-33–stimulated amphiregulin expression and a heightened ability to induce M2-like macrophages. The finding that amphiregulin-producing Tregs have a noneffector phenotype and are progressively lost upon TCR-induced proliferation and differentiation suggests that the tissue repair capacity of human Tregs may be an innate function that operates independently from their classical suppressive function.
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 | 0.000 |
| 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".