Eosinophil lineage commitment and IL-5-dependent expansion is regulated by IL-33 in mice
Bibliographic record
Abstract
Abstract Eosinophils are important in the pathogenesis of many diseases, including asthma, eosinophilic esophagitis and eczema. While IL-5 is necessary for the maturation of eosinophil progenitors (EoP) into mature eosinophils (EoM), the signals that promote commitment to the eosinophil lineage are unknown. The IL-33 receptor, ST2, is expressed on several inflammatory cells, including eosinophils, and is best characterized for its role during the initiation of allergic responses in the peripheral tissues. Recently, ST2 expression was described on hematopoietic stem cells, where its function remains unclear. Here, we sought to determine whether IL-33 and ST2 contribute to hematopoietic lineage decisions. We found that both IL-33- and ST2-deficient mice exhibited diminished peripheral blood eosinophils at baseline. Correspondingly, IL-33 administration increased EoM as well as IL-5 in the blood and bone marrow in WT and IL-33-deficient but not ST2-deficient mice. Blocking IL-5 with a neutralizing antibody prevented IL-33-expanded EoP from maturing into EoM, while transgenic overexpression of IL-5 in ST2-deficient mice resulted in significantly lower hypereosinophilia than transgenic IL-5 mice. Finally, we observed that IL-33, but not IL-5, specifically expanded EoP and upregulated IL-5Rα on EoP as well as increased IL-5 after bone marrow was cultured for three days. Our findings identify a basal defect in eosinophilopoiesis in IL-33- and ST2-deficient mice. Furthermore, we establish unappreciated roles for IL-33 and ST2 in eosinophil development via progenitor regulation and define a mechanism whereby IL-33 licenses commitment into the eosinophil lineage by driving both responsiveness to IL-5 and IL-5 production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".