Tr1 Cells, but Not Foxp3+ Regulatory T Cells, Suppress NLRP3 Inflammasome Activation via an IL-10–Dependent Mechanism
Bibliographic record
Abstract
The two best-characterized types of CD4(+) regulatory T cells (Tregs) are Foxp3(+) Tregs and Foxp3(-) type 1 regulatory (Tr1) cells. The ability of Foxp3(+) Tregs and Tr1 cells to suppress adaptive immune responses is well known, but how these cells regulate innate immunity is less defined. We discovered that CD44(hi)Foxp3(-) T cells from unmanipulated mice are enriched in Tr1 cell precursors, enabling differentiation of cells that express IL-10, as well as Tr1-associated cell surface markers, CD49b and LAG-3, and transcription factors, cMaf, Blimp-1, and AhR. We compared the ability of Tr1 cells versus Foxp3(+) Tregs to suppress IL-1β production from macrophages following LPS and ATP stimulation. Surprisingly, Tr1 cells, but not Foxp3(+) Tregs, inhibited the transcription of pro-IL-1β mRNA, inflammasome-mediated activation of caspase-1, and secretion of mature IL-1β. Consistent with the role for IL-10 in Tr1 cell-mediated suppression, inhibition of inflammasome activation and IL-1β secretion was abrogated in IL-10R-deficient macrophages. Moreover, IL-1β production from macrophages derived from Nlrp3(A350V) knockin mice, which carry a mutation found in cryopyrin-associated periodic syndrome patients, was suppressed by Tr1 cells but not Foxp3(+) Tregs. Using an adoptive transfer model, we found a direct correlation between Tr1 cell engraftment and protection from weight loss in mice expressing a gain-of-function NLRP3. Collectively, these data provide the first evidence for a differential role of Tr1 cells and Foxp3(+) Tregs in regulating innate immune responses. Through their capacity to produce high amounts of IL-10, Tr1 cells may have unique therapeutic effects in disease-associated inflammasome activation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".