Induction of stable human FOXP3<sup>+</sup> Tregs by a parasite‐derived TGF‐β mimic
Bibliographic record
Abstract
Abstract Immune homeostasis in the intestine is tightly controlled by FOXP3+ regulatory T cells (Tregs), defects of which are linked to the development of chronic conditions, such as inflammatory bowel disease (IBD). As a mechanism of immune evasion, several species of intestinal parasites boost Treg activity. The parasite Heligmosomoides polygyrus is known to secrete a molecule (Hp‐TGM) that mimics the ability of TGF‐β to induce FOXP3 expression in CD4+ T cells. The study aimed to investigate whether Hp‐TGM could induce human FOXP3+ Tregs as a potential therapeutic approach for inflammatory diseases. CD4+ T cells from healthy volunteers were expanded in the presence of Hp‐TGM or TGF‐β. Treg induction was measured by flow cytometric detection of FOXP3 and other Treg markers, such as CD25 and CTLA‐4. Epigenetic changes were detected using ChIP‐Seq and pyrosequencing of FOXP3. Treg phenotype stability was assessed following inflammatory cytokine challenge and Treg function was evaluated by cellular co‐culture suppression assays and cytometric bead arrays for secreted cytokines. Hp‐TGM efficiently induced FOXP3 expression (> 60%), in addition to CD25 and CTLA‐4, and caused epigenetic modification of the FOXP3 locus to a greater extent than TGF‐β. Hp‐TGM‐induced Tregs had superior suppressive function compared with TGF‐β‐induced Tregs, and retained their phenotype following exposure to inflammatory cytokines. Furthermore, Hp‐TGM induced a Treg‐like phenotype in in vivo differentiated Th1 and Th17 cells, indicating its potential to re‐program memory cells to enhance immune tolerance. These data indicate Hp‐TGM has potential to be used to generate stable human FOXP3+ Tregs to treat IBD and other inflammatory 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 | 0.000 | 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 | 0.001 |
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".