Inflammatory Effects of Ex Vivo Human Th17 Cells Are Suppressed by Regulatory T Cells
Bibliographic record
Abstract
Th17 cells are proinflammatory cells associated with many immune-mediated diseases. Major factors limiting the study of human Th17 cells are the lack of an accepted method for their in vitro differentiation or for isolation of a homogenous population of Th17 cells that do not cosecrete IFN-gamma. To overcome these hurdles, we established a novel method to isolate in vivo differentiated Th17 cells from peripheral blood by sorting CD161(+)CCR4(+)CCR6(+)CXCR3(-)CD4(+) T cells. The resulting cells produce high levels of IL-17 but not IFN-gamma, express high levels of retinoic acid-related orphan receptor variant 2, and maintain this phenotype upon expansion. Ex vivo Th17 cells exhibit a low cytotoxic potential and are hyporesponsive to polyclonal anti-CD3/anti-CD28 stimulation. Importantly, ex vivo Th17 cells were susceptible to suppression by both naive and memory regulatory T cells (Tregs), which inhibited production of IL-17, IL-22, and CXCL8. Moreover, Tregs suppressed the antifibrotic effects of Th17 cells in a wound-healing model. These findings provide new tools for the study of normal and pathological functions of bona fide Th17 cells in humans. They also provide new insight into the cross-talk between Th17 cells and immune and nonimmune cells, and they establish the paradigm that adoptive Treg-based therapies may effectively limit Th17-mediated inflammation.
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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 | 0.001 | 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) | 0.004 | 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".