IL-33-experienced group 2 innate lymphoid cells in the lung are poised to enhance type 2 inflammation selectively in adult female mice
Bibliographic record
Abstract
While Th2 adaptive immunity has long been considered to orchestrate type 2 inflammation in the allergic lung, group 2 innate lymphoid cells (ILC2s), with the ability to produce a similar profile of type 2 cytokines, likely participate in lung inflammation in allergic asthma. ILC2s are also implicated in sex disparities in asthma, supported by data from murine models showing they are inhibited by male sex hormones. Moreover, larger numbers of ILC2s are present in the lungs of female mice and are correlated with greater type 2 inflammation. Lung ILC2s exhibit intriguing memory-like responses, though whether these differ in males and females does not appear to have been addressed. We have examined type 2 lung inflammation in adult male and female Balb/c mice following delivery of IL-33 to the lung. While the number of ILC2s was elevated equally in males and females four weeks after exposure to IL-33, ILC2s from female mice expressed higher levels of ST2, the IL-33 cognate receptor subunit, and a larger proportion of ILC2s from females expressed the IL-25 receptor (IL-25R), which has previously been linked to memory-like ILC2 responses in mice. Our data show that the subset of ILC2s expressing IL-25R, upon activation, was more likely to produce IL-5 and IL-13. Moreover, STAT6 was absolutely required for enhanced responsiveness in this model system. Altogether, our data show that enhanced type 2 inflammation in females is linked to durable changes in ILC2 subsets with the ability to respond more robustly, in a STAT6-dependent manner, upon secondary activation by innate epithelial-derived cytokines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".