IL-33 activates ILC2 expansion and modulates endometriosis
Bibliographic record
Abstract
Abstract Introduction: Chronic inflammation drives endometriosis (EM) and its associated symptoms. Interleukin (IL)-33, has gained therapeutic interest as it is elevated in the plasma, peritoneal fluid (PF), and lesions of EM patients. However, how IL-33 contributes to EM is largely unknown. For the first time, we demonstrate, using both human samples and murine models, that IL-33 expands local group 2 innate lymphoid cells (ILC2s) and that IL-33 activated ILC2s modulate hallmark features of EM. Methods: We identified ILC2s (CD45+Lin−CRTH2+CD127+ST2+) in the PF of EM patients using flow cytometry. Using immunofluorescence, we determine whether IL-33 forms epithelial or stromal cell niches within the EM lesion. Then, using our well established EM model, we induced EM in female C57Bl/6 mice and treated with PBS or IL-33. Hallmark features of EM were assessed including inflammation (using multiplex cytokine analysis), immune cell profiling (using flow cytometry), and lesion architecture (using immunohistochemistry and Nanostring transcriptomic profiling). To establish cause and effect, we treated ILC2 deficient mice with PBS (n=5) or IL-33 (n=5). Unpaired t-tests and one-way ANOVAs were done when appropriate. Results/Discussion: In humans, ILC2s are present and elevated in EM patients (n=5) compared to healthy donors (n=3) and IL-33 colocalized with both epithelial and stromal cells within the EM lesion. Using murine models, IL-33 drove hallmark features of EM including increased inflammation, alterations in PF immune cells (including eosinophils, T cells, macrophages and ILC2s), as well as increased lesion proliferation, neurogenesis and fibrosis. Using ILC2 deficient mice, we show that IL-33 induced pathology is ILC2 dependent.
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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) | 0.002 | 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".