Helminth Products Protect against Autoimmunity via Innate Type 2 Cytokines IL-5 and IL-33, Which Promote Eosinophilia
Bibliographic record
Abstract
Epidemiologic studies in humans have demonstrated that infection with helminth parasites is associated with a reduced risk of developing autoimmune diseases. Mechanistic studies in mice have linked the protective effect of helminths on autoimmunity to the suppressive activity of helminth-induced regulatory T cells (Tregs) or Th2 cells. In this study, we demonstrate that treatment of mice with Fasciola hepatica excretory-secretory products (FHES) attenuated the clinical signs of experimental autoimmune encephalomyelitis (EAE), a mouse model of multiple sclerosis. Protection was associated with a significant reduction in the infiltration of pathogenic Th1 and Th17 cells into the brain. Although FHES enhanced anti-inflammatory cytokine and Th2 responses, protection against EAE was independent of IL-4, IL-10, and Tregs. However, administration of FHES induced production of the type 2 cytokines IL-33 and IL-5, which promoted accumulation of eosinophils. FHES-induced expansion of eosinophils and protection against EAE was lost in IL-33(-/-) mice and upon neutralization of IL-5. Furthermore, transfer of FHES-induced or IL-33-induced eosinophils conferred protection against EAE. In addition, treatment of mice with recombinant IL-33 attenuated autoimmunity, and this was dependent on IL-5. To our knowledge, this study is the first to report a role for helminth-induced IL-5 and IL-33 in protection against autoimmunity.
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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 | 0.000 |
| 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".