TGF-β1 modulates Foxp3 expression and regulatory activity in distinct CD4+ T cell subsets
Bibliographic record
Abstract
Although forkhead box p3 (Foxp3) expression is restricted to naturally occurring CD4(+) regulatory T cells (T(REG)), little is known about the various signals that regulate it in T cells. As TGF-beta has been reported to modulate Foxp3 expression in T cells, we investigated its effects on the induction or maintenance of regulatory functions in different CD4(+) T cell subsets. TGF-beta1 priming was able to promote differentiation of T(REG) cells from nonregulatory CD4(+)CD25(-) T cells in a concentration-dependent manner through Foxp3 induction. As CD4(+)CD25(-) T cells remain a highly heterogeneous population with variable degrees of antigen experience, we then examined the effect of TGF-beta1 on naive CD4(+)CD25(-)CD45RB(HIGH) T cells. Freshly isolated or TGF-beta1-treated CD4(+)CD25(-)CD45RB(HIGH) T cells never displayed any regulatory functions or significant Foxp3 expression following TCR activation. In stark contrast, freshly isolated CD4(+)CD25(-)CD45RB(LOW) cells, albeit expressing low levels of Foxp3 mRNA and protein, were unable to suppress CD4(+) effector T cell proliferation but acquired regulatory activity and de novo Foxp3 expression following TGF-beta1 exposure. Furthermore, suppression was IL-10-dependent, as anti-IL-10 receptor antibody treatment abrogated this suppression completely, consistent with the ability of TGF-beta1-treated CD4(+)CD25(-)CD45RB(LOW) to synthesize IL-10 upon restimulation in vitro. Last, we show that TGF-beta1 treatment or blockade did not lead to enhanced expansion or function of naturally occurring CD4(+)CD25(+) T(REG) cells, although it maintained Foxp3 mRNA and protein expression. Altogether, TGF-beta1 promotes the induction of IL-10-secreting CD4(+) T(REG) cells from CD4(+)CD25(-)CD45RB(LOW) precursors through de novo Foxp3 production and maintains natural T(REG) cell peripheral homeostasis by sustaining Foxp3 expression.
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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 | 0.000 |
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".