Neutrophil extracellular trap production and CCL4L2 expression influence corticosteroid response in asthma
Bibliographic record
Abstract
The association between neutrophil extracellular traps (NETs) and response to inhaled corticosteroids (ICS) in asthma is unclear. To better understand this relationship, we analyzed the blood transcriptomes from children with controlled and uncontrolled asthma in the Taiwanese Consortium of Childhood Asthma Study using weighted gene coexpression network analysis and pathway enrichment methods. We identified 298 uncontrolled asthma-specific differentially expressed genes and one gene module associated with neutrophil-mediated immunity, highlighting a potential role for neutrophils in uncontrolled asthma. We also found that NET abundance was associated with nonresponse to ICS in patients. In a neutrophilic airway inflammation murine model, steroid treatment could not suppress neutrophilic inflammation and airway hyperreactivity. However, NET disruption with deoxyribonuclease I (DNase I) efficiently inhibited airway hyperreactivity and inflammation. Using neutrophil-specific transcriptomic profiles, we found that CCL4L2 was associated with ICS nonresponse in asthma, which was validated in human and murine lung tissue. CCL4L2 expression was also negatively correlated with pulmonary function change after ICS treatment. In summary, steroids fail to suppress neutrophilic airway inflammation, highlighting the potential need to use alternative therapies such as leukotriene receptor antagonists or DNase I that target the neutrophil-associated phenotype. Furthermore, these results highlight CCL4L2 as a potential therapeutic target for individuals with asthma refractory to ICS.
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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".