Distinct functions and transcriptional signatures in orally induced regulatory T cell populations
Bibliographic record
Abstract
Oral administration of antigen induces regulatory T cells (Treg) that can not only control local immune responses in the small intestine, but also traffic to the central immune system to deliver systemic suppression. Employing murine models of the inherited bleeding disorder hemophilia, we find that oral antigen administration induces three CD4+ Treg subsets, namely FoxP3+LAP-, FoxP3+LAP+, and FoxP3-LAP+. These T cells act in concert to suppress systemic antibody production induced by therapeutic protein administration. Whilst both FoxP3+LAP+ and FoxP3-LAP+ CD4+ T cells express membrane-bound TGF-β (latency associated peptide, LAP), phenotypic, functional, and single cell transcriptomic analyses reveal distinct characteristics in the two subsets. As judged by an increase in IL-2Rα and TCR signaling, elevated expression of co-inhibitory receptor molecules and upregulation of the TGFβ and IL-10 signaling pathways, FoxP3+LAP+ cells are an activated form of FoxP3+LAP- Treg. Whereas FoxP3-LAP+ cells express low levels of genes involved in TCR signaling or co-stimulation, engagement of the AP-1 complex members Jun/Fos and Atf3 is most prominent, consistent with potent IL-10 production. Single cell transcriptomic analysis further reveals that engagement of the Jun/Fos transcription factors is requisite for mediating TGFβ expression. This can occur via an Il2ra dependent or independent process in FoxP3+LAP+ or FoxP3-LAP+ cells respectively. Surprisingly, both FoxP3+LAP+ and FoxP3-LAP+ cells potently suppress and induce FoxP3 expression in CD4+ conventional T cells. In this process, FoxP3-LAP+ cells may themselves convert to FoxP3+ Treg. We conclude that orally induced suppression is dependent on multiple regulatory cell types with complementary and interconnected roles.
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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".