A population of c-kit <sup>+</sup> IL-17A <sup>+</sup> ILC2s in sputum from individuals with severe asthma supports ILC2 to ILC3 trans-differentiation
Bibliographic record
Abstract
In prednisone-dependent severe asthma, uncontrolled sputum eosinophilia is associated with increased numbers of group 2 innate lymphoid cells (ILC2s). These cells represent a relatively steroid-insensitive source of interleukin-5 (IL-5) and IL-13 and are considered critical drivers of asthma pathology. The abundance of ILC subgroups in severe asthma with neutrophilic or mixed granulocytic (both eosinophilic and neutrophilic) airway inflammation, prone to recurrent infective exacerbations, remains unclear. Here, we found by flow cytometry that sputum ILC3s are increased in severe asthma with intense airway neutrophilia, whereas equivalently raised sputum ILC2s and ILC3s were found in severe asthma with mixed granulocytic inflammation. Unbiased clustering analyses identified an “intermediate-ILC2” population displaying markers of both ILC2s (prostaglandin D 2 receptor 2; CRTH2, IL-5, and IL-13) and ILC3s (c-kit and IL-17A) that were most abundant in severe asthma with mixed granulocytic airway inflammation. Intermediate ILC2s correlated with airway neutrophilia and were associated with increased amounts of IL-1β and IL-18 in sputum supernatants. Coculture of sort-purified canonical ILC2s with IL-1β and IL-18 in vitro up-regulated c-kit and IL-17A as well as gene expression profiles related to both type 2 and type 17 inflammatory pathways. Together, we have identified an intermediate-ILC2 phenotype in the airways of individuals with severe mixed granulocytic asthma, representing a candidate therapeutic target for controlling neutrophilic airway inflammation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".