Human RORγt <sup>+</sup> T <sub>H</sub> 17 cells preferentially differentiate from naive FOXP3 <sup>+</sup> Treg in the presence of lineage-specific polarizing factors
Bibliographic record
Abstract
RORγt(+) T(H)17 cells are a proinflammatory CD4(+) T-cell population associated with autoimmune tissue injury. In mice, priming of T(H)17 requires TGF-β, which alone directs the priming of FOXP3(+) regulatory T cells (Treg), in association with inflammatory cytokines. Priming of human T(H)17 cells from conventional naive CD4(+) T cells under similar conditions, however, has proved difficult to achieve. Here, we report that differentiation of human T(H)17 cells preferentially occurs from FOXP3(+) naive Treg (NTreg) in the presence of IL-2 and IL-1β and is increased by IL-23 and TGF-β. IL-1β-mediated differentiation correlated with IL-1RI expression in stimulated NTreg and was accompanied by induction of RORγt along with down-regulation of FOXP3. IL-17-secreting cells in NTreg cultures cosecreted TNF-α and IL-2 and contained distinct subpopulations cosecreting or not cosecreting IFN-γ and other T(H)17-associated cytokines. Polarized NTreg contained significant subpopulations of CCR6-expressing cells that were highly enriched in IL-17-secreting cells. Finally, analysis of CCR6 expression with respect to that of IL-1RI identified distinct IL-17-secreting subpopulations that had maintained or lost their suppressive functions. Together our results support the concept that priming of human T(H)17 from naive CD4(+) T cells preferentially takes place from FOXP3(+) Treg precursors in the presence of lineage-specific polarizing factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".