The alarmins IL-1 and IL-33 differentially regulate the functional specialisation of Foxp3+ regulatory T cells during mucosal inflammation
Bibliographic record
Abstract
CD4 + Foxp3 + regulatory T (T REG ) cells are critical mediators of peripheral tolerance and modulators of immune responses. Functional adaptation of T REG cells, through acquisition of secondary transcription factors is critical for their effector differentiation towards local inflammatory stimuli including infections. The drivers and consequences of this adaptation of T REG cell function remain largely unknown. Using an unbiased screen, we identified receptors of the IL-1 family controlling the adaptation of T REG cells. Through respiratory infection models, we show that the IL-33 receptor (ST2) and the IL-1 receptor (IL1R1) selectively identify stable and unstable T REG cells at mucosal surfaces, respectively. IL-33, not IL-1, is specifically required for maintaining the suppressive function of T REG cells. In the absence of ST2, T REG cells are prone to lose Foxp3 expression and acquire RORγT and IL1R1, while, in the absence of IL-1R1, they maintain Foxp3 expression and resist the acquisition of a Th17 phenotype. Finally, lack of IL-1 signalling enhances the accumulation of ST2 + T REG over pro-inflammatory T REG cells in a Cryptococcus neoformans infection. These observations show that IL-1 and IL-33 exert opposing functions in controlling the functional adaptation of T REG cells, ultimately dictating the dynamics of adaptive immunity to pathogens.
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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".